NTT Technical Review

June 2018 Vol. 16 No. 6
Feature Articles: Creating a Flexible and Smart Network as a New Social Infrastructure

Future Network Technologies for the 5G/IoT Era



Leveraging General-purpose Technology and Open Community Activities



Creating New Value by Leveraging Network-AI Technology in Service Operations



Initiatives toward Access Network Technology for the Beyond-5G Era



Research toward Realizing a Future Network Architecture

MOOSIA: Technology for One-stop Operation

Global Collaboration Initiatives Revolutionizing Research and Development of Network
Technologies

Regular Articles
Traffic Prediction and Quality Evaluation for Improving Communication Quality during Major
Events

Investigating Countermeasures against High-altitude Electromagnetic Pulse (HEMP)

Global Standardization Activities

Automotive Edge Computing Consortium—a Global Effort to Develop a Connected Car
Platform

Practical Field Information about Telecommunication Technologies


Investigation of Unintentional One-ring Outgoing Calls

Short ReportsSuccessful Joint Test of Next-generation Onboard IoT Platform

External Awards/Papers Published in Technical Journals and Conference ProceedingsExternal Awards/Papers Published in Technical Journals and Conference Proceedings

Feature Articles: Creating a Flexible and Smart
Network as a New Social Infrastructure

Future Network Technologies for the
5G/IoT Era
Yukari Tsuji, Arata Itoh, and Masaki Kobayashi
Abstract

The social environment is changing rapidly, and with these changes, society is coming to have great
expectations for the future network. NTT is researching and developing technologies for achieving aflexible and smart network as a new social infrastructure that will be able to meet a wide variety of needs.
This article describes the direction of research and development at NTT with an eye to 2020 and beyond
centered on the three pillars of 5G/IoT (fifth-generation mobile communications/Internet of Things),
Network-AI (NTT’s artificial intelligence applied to networks), and open source software, while providing an overview of activities done in collaboration with NTT Group companies.
Keywords: 5G/IoT, Network-AI, OSS

1. Environmental changes and social issues
surrounding the network
The social environment and structure are changing
against a background of technical innovations andparadigm shifts in many fields. The field of information and communication technology (ICT) is no
exception. Numerous social problems and issues inbusiness operations are occurring due to external factors such as globalization, a decline in the workingage population, climate variability, natural disasters,
and internal factors originating within the network
such as digitization, the explosive growth in Internet
traffic, and cyberattacks. Despite these issues, theICT industry is rapidly entering an era highlighted by
the convergence of communications and information
thanks to the Internet and by co-creation driven bymobile communications and cloud computing. In
response to these changes, NTT has defined a business-to-business-to-X (B2B2X) model, and with aneye to new business and market formats, it has undertaken the research and development (R&D) of technologies to support changes in the business models ofservice providers and changes in lifestyle brought onby subsequent creation of new value.
In this era of change, we consider fifth-generation
mobile communications and Internet of Things (5G/
1

IoT) and artificial intelligence applied to networks(Network-AI) to be key underlying technologies forachieving a smart and flexible network to meet thediverse demands of society and to solve the social andbusiness issues surrounding the network. Additionally, as part of this flow, we are studying open source
software (OSS) for use as the core technology in thetransformation of network system development at
NTT. We therefore treat these three technologies aspillars of R&D targeting the NTT network system. In
this article, we describe how each of these three key
technologies relates to the NTT network, and wetouch on future challenges.
2. Future network architecture toward the
5G/IoT era
In anticipation of the arrival of the 5G/IoT and
B2B2X era, NTT aims to contribute to new services
and value creation by providing many and varied
functions desired by diverse service providers indiverse industries in a simple and speedy manner [1].
Here, the virtualization of network functions (softwarization) is an underlying technology to be implemented in the next-generation network. In recent years,
the R&D of virtualization technologies, namely, network functions virtualization and software-defined
NTT Technical Review

Feature Articles

networking, has been progressing along with thestandardization of interfaces between segmented
functions. A network or computing resource that has
been virtually constructed on a physical resource or
device is called a network slice. To achieve suchslices as part of a telecommunications carrier’s network, the NTT laboratories have undertaken R&D ofslice-management and slice-isolation technologies[2].
In the future, the access network, which serves as
the customer’s gate to services, will provide higher
speeds and capacities, guaranteed and optimal endto-end quality, and flexible placement of functions(including softwarization). The aim here is to provide
high-value-added services and enable new lifestyles
by giving the access facilities supporting the accessnetwork smart design, maintenance, and operation
capabilities [3].
3. Advanced and smart network operation
through AI technologies
In May 2016, the NTT Group unified its AI-related
technologies and some initiatives using those technologies under the brand name corevo® [4]. The AI
technologies that make up corevo are classified into
Agent-AI, Heart-Touching-AI, Ambient-AI, andNetwork-AI. In the following, we report on NetworkAI technologies.
In an environment of diversified ICT services driven by various changes happening in society (e.g., adecline in the working population) and expansion of
the B2B2X business model, the technical development of AI for networks (Network-AI) is progressing
with the aim of making network operation more efficient and providing greater value [5]. The businessareas deemed favorable to the application of Network-AI and issues surrounding its practical use havebeen clarified based on a series of technical developments and field trials.
The NTT laboratories have selected two businessareas for further R&D of Network-AI applications.
The first area is the use of AI to achieve automatedand skill-less processes. In this area, the target is tofind cause-and-effect relationships (rules and logic)
using AI. The second area is the use of AI to provide
human assistance. The target here is to uncoverabnormal trends in a process from massive amountsof log data by using deep learning or other techniques
to give the operator of that process an enhanced senseof awareness.
An example of the former would be using AI toVol. 16 No. 6 June 2018

infer cause-and-effect in the occurrence of alarms andfailures or to visualize work traditionally performedby people and to formulate rules with respect to thatwork. This would be accomplished by using machinelearning to analyze documents that freely record
behavior when responding to a failure and visualizing
the procedures taken in response to a problem(implicit knowledge) as workflow. The goal here is toraise efficiency by having AI do the work that people
have been doing up to now. An example of the latter
would be using AI in failure judgment and proactive(design and) operation that up to now has been basedon threshold values. In particular, AI would be used
to spot the seeds of an abnormal occurrence that ahuman operator has not been capable of noticing andto then predict future behavior and provide feedback
to the operator.
To achieve practical use of Network-AI, it is
becoming clear that (1) correct answers (correct decisions) must be continuously learned, (2) the decisionmaking process is a black box unknown to people,and (3) 100% correct answers cannot be derived.
Here, a mechanism that uses AI while continuously
learning is necessary, and since 100% accuracy indetecting signs of a failure cannot be guaranteed,
there is a need for an operation that uses AI output
skillfully, taking this limitation into account. At thetime of its introduction, AI will not be adept at all
business tasks but will be more like an entry-level
employee that has to learn and develop just like therest of us. However, a day will come when this entrylevel employee starts to provide human employeeswith assistance.
To resolve these issues surrounding the practicaluse of AI, the plan is to further accelerate trials using
actual data (set up learning environments) and to promote a revolution in work style making good use of
AI-based support tools toward advanced and smart
network operation.
4. An open and community-based developmentof network system
The types of participants in the ICT product market
including communications equipment is diversifying
thanks to the growing influence of OTT (over-thetop) operators, the use of ICT products in the communications industry, the increasing percentage ofcommunication functions and services provided bysoftware, and the standardization of procurement
specifications by industry groups. At the same time,the movement promoting the use of OSS is growing
2

Feature Articles

in the network and network-management area while
continuing in the cloud area, and the network-related
OSS community continues to expand.
In response to these changes, the NTT laboratories
are working to transform their network system development techniques. While the future network system
will be based on the right product in the right placeprinciple that places demands on business and service
needs, the use of OSS and white box-type hardware
is expected to increase even further. The NTT laboratories will explore network system development techniques using the wisdom of the open community andwill become actively involved in community activities while leveraging the technological competence
and know-how of a telecommunications carrier [6].
In this way, we hope to reduce development investment costs and to heighten corporate value throughdissemination of our technical capabilities.
From the perspective of those who advocate using
an open network system, the problem arises as to whotakes responsibility for handling frequent version
upgrades and fatal security holes that become evident
in OSS operation. To deal with this problem, theentire NTT Group and global players, including bothdevelopers and operators, need to create and enhance
OSS products and raise the skills of personnel who
use OSS on an ongoing basis. This effort will proceed
in collaboration with NTT Group companies.
Another issue of a medium- and long-term nature is
the building of collaborative and co-creative relationships with commercial vendors that have many andvaried products. Far from becoming fixed on only theopen community, the NTT laboratories seek to establish a scheme of applying the knowledge and techniques obtained through OSS to commercial products
in cooperation with diverse vendors. In this way, we
aim to reduce investment costs and operating expenses and improve service quality through the use ofcommercial products [7, 8].

that the quality, reliability, fault resilience, neutrality,
and security demanded by society be satisfied.
Amid these changes, we can expect an exponential
increase in digital service traffic. The nature of trafficis also bound to change. For example, we can expect
an increase in geographically restricted traffic andinter-device traffic as in IoT. NTT is working to solvethese problems by developing technologies forincreasing the capacity of the optical access and core
networks, controlling quality and traffic on a software-implemented network, and dealing with local
traffic. Multi-access edge computing (MEC) is one
such promising technology. There are growing
expectations that MEC will enable distributed placement of optimal functions by bringing the network
even closer to devices and enabling diverse forms ofaccess. To this end, the technical development of
MEC is progressing.
Going forward, the NTT Group plans to use network, AI, IoT, and security technologies and promote
open collaboration with diverse industries with theaim of solving social problems, increasing industrial
value and competitiveness, and creating a moresecure and enriching society.
References
[1]

[2]

[3]

[4]

5. Challenges for 2020 and beyond
In the world of 2020 and beyond, where cyberspaceand physical space converge, the information andcommunication network will come to support mission-critical social infrastructures. One example ofsuch an infrastructure is connected cars. There is also
no doubt that information and communication networks will support high-definition video transmission, remote medicine, and other functions and will
become increasingly important as new social infrastructures. Becoming a social infrastructure requires
3

[5]

[6]

[7]
[8]

T. Moriya, A. Yoshida, Y. Ito, M. Kobayashi, S. Harada, and S. Horiuchi, “MOOSIA: Technology for One-stop Operation,” NTT Technical
Review, Vol. 16, No. 6, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa6.html
S. Yasukawa, H. Sato, T. Hirota, T. Tojo, K. Endo, Y. Kasahara, and H.
Suzuki, “Research toward Realizing a Future Network Architecture,”
NTT Technical Review, Vol. 16, No. 6, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa5.html
H. Furukawa, T. Kirihara, S. Narikawa, K. Minami, K. Horikawa, S.
Ikeda, and T. Arai, “Initiatives toward Access Network Technology for
the Beyond-5G Era,” NTT Technical Review, Vol. 16, No. 6, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa4.html
T. Yagi and H. Ozawa, “Creation of Artificial Intelligence Services
through Open Innovation,” NTT Technical Review, Vol. 15, No. 8,
2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201708fa1.html
K. Watanabe, Y. Ikeda, Y. Nakano, K. Ishibashi, R. Kawahara, and S.
Suzuki, “Creating New Value by Leveraging Network-AI Technology
in Service Operations,” NTT Technical Review, Vol. 16, No. 6, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa3.html
M. Shimizu, Y. Matsuoka, and J. Sasaki, “Leveraging General-purpose Technology and Open Community Activities,” NTT Technical
Review, Vol. 16, No. 6, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa2.html
Press release issued by NTT on April 17, 2017.
http://www.ntt.co.jp/news2017/1704e/170417a.html
T. Kuwahara, H. Irino, and K. Suzuki, “Global Collaboration Initiatives

NTT Technical Review

Feature Articles

Revolutionizing Research and Development of Network Technologies,” NTT Technical Review, Vol. 16, No. 6, 2018.

Yukari Tsuji

Vice President, NTT Network Technology
Laboratories.
She received a B.S. and M.S. in information
science from the University of Tsukuba, Ibaraki,in 1987 and 1989, and an MBA in the Sloan Fellows Program from Massachusetts Institute ofTechnology, USA, in 2005. She joined NTT in
1989 and began researching and developing
asynchronous transfer mode (ATM) switches andvarious services. She was promoted to Executive
Director of the R&D Center at NTT WEST in
2013. She has been in her present position since
July 2016. She is a member of the Science Council of Japan.

https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa7.html

Masaki Kobayashi

Vice President, NTT Access Network Service
Systems Laboratories.
He received a B.E. in engineering from Hokkaido University in 1989. He joined NTT in 1989
and was primarily involved in facility management and planning of telecommunication systems at NTT EAST. He was a Senior Manager of
the Corporate Strategy Planning Department at
NTT-ME from 2007 to 2010. He also served as a
Senior Manager of the General Affairs and Personnel Department at NTT EAST. He has been in
his current position since July 2017.

Arata Itoh

Vice President, NTT Network Service Systems
Laboratories.
He received a B.E. and M.E. from Tokyo Institute of Technology in 1986 and 1988. Sincejoining NTT in 1988, he has conducted R&D of
ATM based multimedia switching architecture
and NGN (Next Generation Network). He has
also been involved with the NTT EAST/WEST
R&D Center and the R&D vision group of the
NTT R&D Planning Department.

Vol. 16 No. 6 June 20184

Feature Articles: Creating a Flexible and Smart
Network as a New Social Infrastructure

Leveraging General-purpose
Technology and Open Community
Activities
Masafumi Shimizu, Yasuyuki Matsuoka,
and Junko Sasaki
Abstract

The NTT laboratories are revolutionizing their development techniques for network systems in orderto support a collaboration model and respond flexibly to market changes and technology trends. This
article describes our approach to building function groups using open source software and hardware
components and introduces our activities in the open community.
Keywords: OSS, open innovation, community activities

1. Environment surrounding network systems
The environment surrounding network systems canbe described from three key perspectives: a change intechnology trends, a change in trends of Japanese/overseas telecom carriers and vendors, and a change
in business structure.

playing a core role in the global OSS community. The
use of OSS makes it possible to achieve reliability
and long-term stability through inspection of code inopen forums and to achieve a high level of flexibility
by responding to diverse user needs. It therefore promotes greater flexibility and speed in software development.

1.1 Change in technology trends (conversion to
general-purpose technology)
An examination of hardware technology trends in
information and communication technology products
reveals that functions heretofore implemented using
specialized equipment for telecom carriers are now
being achieved using general-purpose devices at anaccelerating rate. In particular, high-performance
general-purpose processors are enabling functions
traditionally processed by hardware to be done bysoftware, and at the same time, hardware and software are becoming increasingly separated throughvirtualization, enabling functions to be implemented
with an even higher degree of freedom. In the datacenter industry, there are many open source software(OSS) packages that can be openly used on a licensing basis. At NTT, we are actively using and contributing to OSS developed within the NTT Group while

1.2 C
 hange in trends of Japanese/overseas telecom carriers and vendors
Compared to the situation in the datacenter industry, equipment development in the telecom-carrier
network field has traditionally been slow, and it has
been costly to achieve large-scale, high-reliability,and high-performance networks.
However, OTT (over-the-top) service providers andthe so-called hyper giants in particular have adopted
open product architecture to create an extensive product market whose application scope is rapidly
expanding from cloud systems to enterprise and network systems. In line with this movement, telecomequipment vendors have begun to use such open
functions when implementing products. Some telecom carriers in Europe and the United States, moreover, have begun to open up their own products andhold community-based implementation-promotion

1

NTT Technical Review

Feature Articles

Improveproductivity andefficiency.

Improve customer
satisfaction.

Users

Achieve rapid service launches and provide
a low-priced, diverse service menu.

Telecom carrier network

OpenStack
KVM

Improve corporate
value; create business
opportunities.

OvS/DPDKRecommended package
Collaboration
Business partner(telecom-equipmentvendor, etc.)

Improve development
speed and flexibility.

Core developers etc.

Disseminate
technical abilities
in an open
environment.

Investors, companies

Open community
Collective
wisdomOTT providers, academic
institutions, companies,
individuals, etc.

CloudEnterprise

Network
OSS

Hardware

DPDK: Data Plane Development Kit
KVM: Kernel-based Virtual Machine
OvS: Open vSwitch

Fig. 1. Revolution in network-system development.

activities with the aim of improving their products
and applying them to telecom carrier networks. As aresult, it appears that the trend toward system development that assumes the embedding of OSS will
accelerate all the more in the field of telecom carrier
networks.
In addition, we can expect the proactive participation of telecom carriers in the OSS community
through contributions and other activities to speed up
the adoption of requirements in the field of telecom
carrier networks. Such activities can enhance a telecom carrier’s visibility among customers, investors,
and business partners, improve corporate value, andcreate new business opportunities.
1.3 Change in business structure
At NTT, the transition from a B2C (business-toconsumer) to a B2B2X (business-to-business-to-X)
model is accelerating, and as a result, there is a growing need to provide prompt and flexible network
functions tailored to the needs of the second B (business partners). Consequently, there is also a need inVol. 16 No. 6 June 2018

the field of telecom carrier networks to achieve functions that meet the needs of customers in a more flexible, cheaper, and faster manner.
2. Revolutionizing network-system developmentusing OSSIn light of the above changes in the business environment, NTT is revolutionizing its network-systemdevelopment techniques to accelerate the modularization and combination of network functions. The
overall goal is to create a flexible network that cangive the service menu more degrees of freedom andfacilitate the speedy provision of services. At NTT,
we seek to accelerate such modular-type development by making a transition from the conventional
in-house development and procurement method to amethod that uses OSS and general-purpose hardware
components and that works to incorporate telecomcarrier requirements in the open-source community
for implementation in OSS (Fig. 1).
The following two issues must be addressed in the2

Application requirement 1

Application requirement 2

Application requirement 3

vRouter

CPE

EPC

NFVI

CommonNetwork
carrierfunctions

Quasiapplication
NFVI

KVM

VIM

NFVI
Scale

VIM
Functions

NFVI

Common

VIM

CommonPerformance

Feature Articles

Quasiapplication
OvS/DPDK

Linux

VIM

OpenStackSelect or combine according toapplication requirements.

Hardware (CPU, storage, FPGA, etc.)
Common-OSS-group recommended package
CPE: customer premises equipment
CPU: central processing unit
EPC: evolved packet core

FPGA: field-programmable gate array
VIM: virtualized infrastructure manager

Fig. 2. Building function groups.

use of OSS:(1)	Accumulating know-how in building functions consisting of OSS components conforming to standard architecture(2)	Finding methods of dealing immediately withmissing OSS functions or software bugs
To resolve these issues, we are building functiongroups and taking part in open-source community
activities as described below.
2.1 Building of function groups
System implementation in the case of a telecom
carrier network requires the provision of functions
that combine a wide variety of hardware and software, products from various companies, and OSS as
well as those in many variations including different
software versions. A telecom carrier network, in particular, requires that an optimal combination of functions be selected to satisfy its functional and nonfunctional requirements. As a means of meeting this need,
we are providing a common-OSS-group recommended package that compiles functions that can beused in common while having a business partner suchas a vendor decide on a recommended pattern accord3

ing to application requirements and provide the associated software.
We consider, for example, cases of building function groups conforming to network functions virtualization (NFV) architecture now being standardized at
the European Telecommunications Standards Institute (ETSI) [1]. Here, a virtual network function(VNF) consisting of diverse network functions runs
above the network functions virtualization infrastructure (NFVI) and management and orchestration(MANO) sections. However, while NFVI and MANO
are affected by VNF requirements, they are notdependent on specific network functions, so they canbe assembled as common functions (Fig. 2).
The main features of this common function groupcomposed of OSS are listed below.
•	A platform that satisfies diverse VNF requirements•	Proactive use of OSS and general-purpose hardware•	An architecture that partially uses required functions•	Use of tools from the datacenter field•	Application of knowledge and know-how related
NTT Technical Review

Feature Articles

Telecom carrier
Users of business-partner
products(incorporating OSS)OSS users

Determine and distribute OSS-group
recommended pattern.

Business partner

NTT laboratories etc.

Collaboration

Software group

Software group

Community activities

OSS community
ONF

TIP

OpenStackONF: Open Networking Foundation
TIP: Telecom Infra Project

Fig. 3. Function-group building cycle.

to quality/maintenance management cultivatedat NTT laboratories•	Guarantee of connectivity to equipment fromdiverse vendors
This common function group can now be provided
with software from a business partner who has determined a recommended pattern according to application requirements. This makes it possible to provide
an operability-confirmed recommended package that
extends as far as the application itself. This can simplify troubleshooting in the event of a problem andthereby reduce any system downtime (Fig. 3). Goingforward, we will continue our efforts in resolving
technical issues and easing requirements with the aimof expanding the use of this common function group.
2.2 Community activities
In system applications to a telecom carrier network,
there will be times when an immediate response isneeded in order to deal with missing OSS functions
or software bugs. We can therefore envision cases inwhich missing functions are temporarily created, but
Vol. 16 No. 6 June 2018

continuing to respond in this manner can result in a
dramatic increase in individual functions. Within the
OSS community, however, publically released OSS
may be objectively used and tested by engineers invarious companies who may then point out software
bugs or functional deficiencies. We can therefore
expect software functions and quality corresponding
to common requirements of telecom carriers to
improve via the OSS community.
With this in mind, we are promoting community
activities from the viewpoints of opening up NTT
research and development products and conveying
requirements to the OSS community. Here, we are
examining existing OSS in terms of compatibility
with telecom-carrier development strategies, degreeof community activity, degree of software completion, and application fields and talking to specificcommunities that have a high affinity with our own
products and a high OSS usage effect. Additionally,
with respect to functions that we judge to be insufficient,
we are conveying telecom-carrier requirements to the
OSS community. The communities that we are4

Feature Articles

Operation Support
System / BSS

CORD

Orchestrator

ONAP

ONF
VNF manager

EMS
ONOS
NFV-converted
network functions
(VNF)

SDN
controller
TIP

Virtual infrastructure(NFV)
Virtualized PF(hypervisor)

SDN
switchOpenStack
OPNFV
DPDK

Virtualinfrastructure
manager (VIM)

Hardware*Revised on basis of ETSI NFV model
BSS: business support system
CORD: Central Office Re-architected as a Datacenter
EMS: element management system
ONAP: Open Network Automation Platform

ONOS: Open Networking Operating System
OPNFV: Open Platform for NFV
PF: platform
SDN: software-defined networking

Fig. 4. Targeted communities.

currently targeting based on this policy are shown in
Fig. 4.
In the area of software-defined networking (SDN)
switches and controllers, we are promoting MultiService Fabric (MSF) [2] technology developed by
the NTT laboratories as part of our activities in the
Telecom Infra Project (TIP) [3], which aims to standardize the next-generation SDN controller satisfying telecom carrier requirements. Similarly, with theaim of popularizing the Lagopus [4] switch developed by NTT laboratories within TIP, we are working
toward a proof of concept demonstration. In OpenNetworking Foundation (ONF) activities [5], ourcontributions include feeding back to the community
the results of an evaluation conducted by the NTT
laboratories on CORD (Central Office Re-architected
as a Datacenter)-3.0 from the viewpoint of a telecom

5carrier. Additionally, in the area of virtual infrastructure and virtual infrastructure management, we aremaking contributions to OpenStack [6], Open Platform for NFV (OPNFV) [7], and the Data Plane
Development Kit (DPDK) [8]. At OPNFV, we arefollowing up our activities in resource reservation
technology in the Promise project with upstream
activities in the OpenStack Blazar project.
References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]

ETSI, http://www.etsi.org/
MSF, https://github.com/multi-service-fabric/msf
TIP, http://telecominfraproject.com/
Lagopus, http://www.lagopus.org/
ONF, https://www.opennetworking.org/
OpenStack, https://www.openstack.org/
OPNFV, https://www.opnfv.org/
DPDK, http://dpdk.org/

NTT Technical Review

Feature Articles

Masafumi Shimizu

Senior Research Engineer, Network Systems
Planning & Innovation Project, NTT Network
Service Systems Laboratories.
He received a B.E. and M.E. in electrical engineering from Tokyo Institute of Technology in1999 and 2001. He joined NTT Network Service
Systems Laboratories in 2001 and studied IP/
MPLS (Internet protocol/multiprotocol label
switching) network system architecture. He wasthen engaged in developing IP network management systems of the Next Generation Network(NGN). During 2013–2017, he was with the
R&D Vision Group, R&D Planning Department.
His current interests include network virtualization technologies such as SDN and NFV, andOSS strategies. He received the Institute of Electronics, Information and Communication Engineers (IEICE) Technical Committee on Information and Communication Management researchaward in 2012.

Junko Sasaki

Director, Network Systems Planning & Innovation Project, NTT Network Service Systems
Laboratories.
She received a B.E. and M.E. in engineeringfrom Osaka University in 1990 and 1992. She
joined NTT in 1992 and moved to NTT Network
Service Systems Laboratories in 2007. Her current interests include network virtualization
technologies such as SDN and NFV, and OSS
strategies.

Yasuyuki Matsuoka

Senior Research Engineer, Network Systems
Planning & Innovation Project, NTT Network
Service Systems Laboratories.
He received a B.S. and M.S. in physics fromthe University of Tokyo in 1998 and 2000. He
joined NTT Network Service Systems Laboratories in 2000, where he was involved in network
design and systems development to launch the
NGN. His current research is focused on virtualized networking and distributed computing
technologies. He is a member of IEICE.

Vol. 16 No. 6 June 20186

Feature Articles: Creating a Flexible and Smart
Network as a New Social Infrastructure

Creating New Value by Leveraging
Network-AI Technology in Service
Operations
Keishiro Watanabe, Yasuhiro Ikeda, Yuusuke Nakano,
Keisuke Ishibashi, Ryoichi Kawahara,
and Satoshi Suzuki
Abstract

We present in this article an overview of recent research and development done at the NTT laboratories
on artificial intelligence technology for networks (Network-AI) that enables proactive maintenance andoperations of network services. We also describe some of the key technologies constituting Network-AI
and review verification trials of these technologies conducted at an NTT operating company.
Keywords: Network-AI, proactive maintenance and operations, automation

1. Introduction
In order to forge ahead with the development ofservices while sustaining the services currently available in the face of social changes such as a declining
population and ever more diversified communication
services, we must have the ability to accurately assess
the operational status of services and be able toupgrade and enhance services once they are up andrunning. The NTT laboratories strive to make service
operations more efficient and to enhance service
value and are therefore working to implement anautonomous control loop that cycles through the threephases of (1) gathering various types of information,(2) analyzing the collected information, and finally,(3) issuing accurate instructions and controls basedon the analytical results in the planning, design, construction, maintenance, and operation of networks.
By leveraging artificial intelligence technology for
networks (Network-AI)*, we are now making good
progress in developing more sophisticated operations
and support systems.
Various Network-AI related initiatives are now
under consideration. One such initiative is resources1

on-demand, which automatically suggests and allocates optimal resources required by service providers. Another is scheduled maintenance, which eliminates the need for urgent maintenance once a network
problem has already occurred. This will give us theability to autonomously control the entire sequenceof service events, from provisioning of services tomaintenance and operations, and the entire NTT laboratory community is working toward this objective.
This article focuses on key initiatives now underwaywith the goal of implementing scheduled maintenance.
2. Initiatives enabling scheduled maintenanceTwo key capabilities are needed to make scheduled
maintenance a practical reality. First, this would
require a shift from reactive maintenance and operations that deals with faults and operational problems
that have already occurred to proactive maintenance*Network-AI: The NTT Group has adopted the brand name corevo
for all its artificial intelligence (AI)-related research and development initiatives. Network-AI is one of the key elements of corevo.

NTT Technical Review

Feature Articles

External network data

Virtual network

Routing control

Virtualserver

Virtual router

Switch function
configuration

Internal network data

Congestion

Fault

Physical network
Observation data

Control data
MonitorAnalyze

Control, recoverCongestion detection

Factor identification
Location estimation

Routing control
Switch function
configuration

Impact assessment

Fault response

Fault detection

Fig. 1. Proactive controlled network [1].

and operations that focuses on preventing problems
before they occur. Second, it requires systematic
automated maintenance and operations that fully supports virtualization. The NTT laboratories have comeup with the proactive controlled network concept as away of implementing proactive maintenance andoperations combined with automation. The idea
behind a proactive controlled network is to anticipate
potential performance degradation risks (e.g., outages, failures, and congestion) before they occur, toanticipate foreseeable changes in demand early on,
and to achieve proactive control and early automatic
recovery.
The proactive controlled network involves asequence of typical operational phases for dealing
with each type of risk: (1) monitor, (2) analyze, and(3) control and recover, as illustrated in Fig. 1 [1], andwe are developing key Network-AI technologies foreach phase with the following objectives:(1)	Achieve proactive early detection of changes
in network conditions due to degraded performance (congestion, equipment failure, etc.).
(2)	Estimate degraded location and likely cause of
the changes in network conditions.
(3)	Take control to circumvent performance degradation, and implement early recovery.
Vol. 16 No. 6 June 2018

Some of the core technologies being applied to
implement these operational phases are indicated inFig. 2.
We focus here on two of these technologies—network anomaly detection, a monitoring technology
developed for early detection of anomalous events(e.g., silent failures), and automatic failure points
estimation, an analysis technology. We also review
the status of ongoing verification trials of the network
anomaly detection technology that has been deployed
and is now being evaluated at an NTT operating company.
3. Network anomaly detection technology
NTT Network Technology Laboratories is making
headway in the development of an autoencoder (AE)based network anomaly detection system designed toachieve early detection of changes in network conditions [2–4]. An AE is a kind of neural network capable of unsupervised learning of the intrinsic complex
structure of data and is currently drawing a great dealof interest for anomaly detection applications.
We have utilized AE characteristics such that by
setting the number of neurons in the hidden layer ofthe AE at less than those in the input layer, a data
2

Feature Articles

Described in detail in this article

Examples of
Network-AI
technology

Syslog analysis

Anomaly detection

Causal analysis

Workflow visualization

Modeling and visualizationof network status logs

Detecting NW state changes
from various data resources

Identifying deterioration
factors and fault locations
from QoS data and alarms

Extracting handling criteria,
fault causes, and handlingprocedures

AI element
technology

Clustering

Modeling /Feature extraction
NW domainknowledge/know-howDataresources

DNN

Outlierdetection

Templating

Learning data extraction

Extract, identify significant
features from NW
equipment log data.

Extract features for efficientlearning of normal statefrom NW data.

NW equip.
log data

Serverlog data

Local data
(temp. etc.)

Topology
data

NetFlowdata

Sparsemodeling

Featurepattern
extraction

Optimization
theoryHMM

Degradation extraction /Automatic rule generation

Description content
decision

Extract factors related to lossof quality and fault location.

Extract worker actions fromwork history data.

QoE/QoSdata

Workhistorydata

O&M
history data

User
behavior
data

Externaltraffic data

* AI element technologies above are just examples of our technologies.
DNN: deep neural network
HMM: hidden Markov model
NW: network
O&M: operation and management

QoE: quality of experience
QoS: quality of service
Temp.: temperature

Fig. 2. Core technologies for achieving proactive controlled network.

(a) Learning

Input
data

(b) Testing

x1

x1

x2

x2

x3

x3

x4

x4

x5

x5

x6

x6

Output
data

Input
data

Layer 1 Layer 2 Layer 3

x1

x1

x2

x2

x3

x3

x4

x4

x5

x5

x6

x6

Outputdata

Layer 1 Layer 2 Layer 3

Decreasing distance between
input and output during learning phase

Output as degree of anomaly

Fig. 3. AE-based anomaly detection.

dimension reduction occurs in the hidden layer bylearning parameters to reconstruct the input layer
data at the output layer. AE-based anomaly detection
is based on the assumption that a normal data distribution is concentrated near a low-dimensional manifold in the input data space. During the learning phaseshown in Fig. 3(a), the normal state is learned by
3

observing various types of data during normal operation of the network, while in the test or anomaly
detection phase shown in Fig. 3(b), current data are
input to the AE learned as described above, and the
distance between input and output layer vectors is
output as the degree of anomaly. If the degree of
anomaly exceeds a certain threshold, it is considered
NTT Technical Review

Feature Articles

Display failure
point candidates.

1st candidate
No analysis ordiagnosis
2nd candidate

Recordingcorrespondence
results

Operators
Failure points estimation system

Rules
Rule learning

Monitoring system

Anomaly detection events
AlarmsNetworkFig. 4. Automatic failure points estimation system.

anomalous.
For our purposes, the network data that are input
include numerical data such as resource/traffic data
based on Simple Network Management Protocol andManagement Information Base (SNMP/MIB) andflow data based on NetFlow as well as detailed text
logs such as syslog data from routers and servers. To
input syslog data to AE, we employ syslog analysis[5] to generate identifiers (IDs) from each syslog line,
and text data are converted to numerical data based onmessage ID frequency analysis.
In addition to anomaly detection, efforts are also
underway to identify the outlier factor when an
anomaly is detected [6]. For example, if an anomaly
is detected by AE, we estimate which input dimension is responsible for increasing the degree of
anomaly by using a sparse-optimization technique.
Calculating the contribution of each input dimension
to the anomaly should make it much easier to isolate
the problem after an anomaly is detected.
4. Automatic failure points
estimation technology
Meanwhile, NTT Access Network Service Systems
Laboratories is working on a related analyticalscheme for locating network anomalies [7], as illustrated in Fig. 4. This system is designed to autonoVol. 16 No. 6 June 2018

mously derive causal links between failure causes
and alarms, or rules, from network information andalarms emitted by equipment that approximately
locate the point of the failure. When a network anomaly is detected as described in the previous section, adetection event and proximate alarms generate a rule,
which accurately predicts the points of the failure.
This information is then used to send a command tocircumvent the trouble and thus avoid a performanceslowdown and/or commence rapid recovery of theservice. Moreover, because new rules created to deal
with new anomalies and failures are saved, the predictive accuracy of the system should improve overtime.
5. Verification trials of network
anomaly detection
We are now in the process of evaluating the network
anomaly detection technology using real operational
data from a testbed and actual services in collaboration with NTT operating companies. This will help us
assess the effectiveness of our proposal and identify
challenges that still need to be addressed. The trials
now being conducted in collaboration with NTT
Communications are described in this section.

4

Degree of anomaly

Feature Articles

Time (approx. 2 months of data)* Red lines: faults reported by trouble tickets

Fig. 5. Results of trial conducted with the Technology Development Department.

5.1 C
 ollaboration with Technology Development
Department
The Technology Development Department of NTT
Communications operates a testbed that has been
made available to NTT Communications for thedevelopment of their services. The department is also
developing the Data Science Lab (DSL) on the testbed, which is a big-data analysis platform initiative tocollect and analyze various types of service and infrastructure data with the idea of exploiting the data tocreate new service strategies and implement anomaly
detection.
For this work, we evaluated the network anomaly
detection technology developed by NTT Network
Technology Laboratories on a range of typical operational data gathered on the DSL—SNMP/MIB, NetFlow, syslogs, trouble tickets, and the like—to assess
the technology’s practical performance and problems.
The basic architecture of the DSL is that of a reproducible analysis/research platform, which can reproduce analytical results and infrastructure as code.
NTT Network Technology Laboratories’ anomaly
detection technology is currently containerized, andwe are now working to integrate it on the DSL infrastructure.
In Fig. 5, we show anomaly data measured over a
two-month time frame, with the degree of anomaly(blue line) on the vertical axis versus time on thehorizontal axis. The vertical red lines indicate problems identified from trouble tickets issued during the
testbed trials. The trouble tickets identify a range ofproblems—a server breakdown, a bug, a denial-ofservice attack, and so on—and they agree remarkably
well with the serious anomaly time slots shown on thegraph.
However, there were other serious anomaly time5

slots detected with the anomaly detection technologythat were not caught by the trouble tickets, so we areworking to verify the effectiveness of the technology
while trying to match up the detection results against
actual incidents and events that occurred. In addition,
there are a number of practical issues that must be
resolved if we are to continually operate the anomaly
detection technology on a real-time basis, and we are
now addressing these issues.
5.2 Collaboration with Network Services Department
The Network Services Department of NTT Communications is currently trying to exploit AI toupgrade the service operations of a whole range ofexisting network services. For example, the MVNO
(mobile virtual network operator) service that has
attracted considerable interest and become immensely popular is a case in point, as the department seeks
to exploit AI to implement anomaly detection in thisservice. More specifically, we have been experimenting with applying network anomaly detection to various types of resource data (utilization of central processing unit (CPU), memory, disk input/output, andso on.) on NTT Communications’ virtualized service
platform to see if we can detect singular events andshifts in the degree of anomaly during normal operation.
As a result, we found that it was possible to detect
singular events from the degree of anomaly by AE
and from the contribution of the input dimension(various kinds of time series numerical data) to theanomaly. We also observed a tendency for the anomaly degree to persist and to gradually change overlonger time spans, and we were able to capturechanges in system behavior manifested as changes inthe degree of abnormality. In addition, we identified
NTT Technical Review

Feature Articles

the specific input dimension causing the change inbehavior by calculating to what degree the AE input
dimension contributes to the anomaly.
From these analyses, we recognized the need for amechanism that tracks system configuration changes(switching between act and standby, ID changes of
the CPU used, and so on) and system behavior changes (changes in CPU, memory, traffic, and other patterns). Therefore, we are developing a statistical,
quantitative, and relearning technology to implement
such a mechanism. We also found that the new technology is capable of visualizing a range of beforeand-after system state changes by clearly showing
monitoring parameter trend changes when the system
is upgraded.
We learned a great deal from carrying out thisassessment. Work in the months ahead will focus onpractically assessing anomaly detection using greater
amounts of data, enhancing our interpretive capabilities after an anomaly has been detected, further testing and assessing the technology, and making efforts
to build a more user-friendly implementation of thetechnology suitable for business environments.

network anomaly detection in particular, we will continue to refine and upgrade this technology, conduct
further verification trials in cooperation with NTT
operating companies, lay the groundwork for prototypes, and eventually implement services using thetechnology. Network anomaly detection still faces a
number of hurdles before it is ready for actual deployment. Most notably, we must improve our ability tointerpret the factors involved when an anomaly isdetected, and we must adapt the technology for use indifferent environments. Needless to say, we are hard
at work in resolving these issues.
References
[1]

[2]

[3]

6. Future development[4]

We presented a broad overview of some of NTT
laboratories’ latest initiatives in the area of NetworkAI, focusing on two key technologies—network
anomaly detection and automatic failure points estimation—and briefly described verification trials of
the network anomaly detection technology done incollaboration with an NTT operating company.
Network-AI clearly has enormous potential, andwe are committed to following through with more
research and development in this area. With regard to[5]

Vol. 16 No. 6 June 2018

[6]

[7]

S. Harada, K. Watanabe, Y. Nakano, Y. Ikeda, Y. Matsuo, M. Kobaya­
shi, A. Suzuki, and R. Kawahara, “An Introduction of Efforts to Realize Proactive Network Control,” Proc. of the 2017 IEICE General
Conference, B-7-32, Nagoya, Aichi, Japan, Mar. 2017 (in Japanese).
Y. Nakano, Y. Ikeda, K. Watanabe, K. Ishibashi, and R. Kawahara,“Autoencoder Based Detection Method for Network Anomalies,”
Proc. of the 2017 IEICE General Conference, B-7-33, Nagoya, Aichi,
Japan, Mar. 2017 (in Japanese).
Y. Ikeda, Y. Nakano, K. Watanabe, K. Ishibashi, and R. Kawahara, “A
Study of Accuracy Improvement on Network Anomaly Detection withAutoencoder,” Proc. of the 2017 IEICE General Conference, B-7-34,
Nagoya, Aichi, Japan, Mar. 2017 (in Japanese).
R. Kawahara, “Application of AI/Machine Learning to Enhance Network Operation/Control Technologies,” Proc. of the 2017 IEICE
Society Conference, BT-2-1, Tokyo, Japan, Sept. 2017 (in Japanese).
T. Kimura, A. Watanabe, T. Toyono, and K. Ishibashi, “Proactive
Failure Detection Learning Generation Patterns of Large-scale Network Logs,” Proc. of the 11th International Conference on Network
and Service Management (CNSM 2015), Barcelona, Spain, Nov.
2015.
Y. Ikeda, K. Ishibashi, Y. Nakano, K. Watanabe, and R. Kawahara,“Inferring Causal Parameters of Anomalies Detected by Autoencoder
Using Sparse Optimization,” IEICE Technical Report, Vol. 117, No.
89, pp. 61–66, 2017 (in Japanese).
K. Itoi, T. Yakawa, H. Oishi, and K. Okazaki, “Automatic Failure
Points Estimation Technology for Generating Knowledge and Rapid
Recovering Operations,” NTT Technical Journal, Vol. 29, No. 5, pp.
60–64, 2017 (in Japanese).

6Feature Articles

Keishiro Watanabe

Senior Research Engineer, Traffic EngineeringGroup, Communication Traffic & Service Quality Project, NTT Network Technology Laboratories.
He received a B.E. and M.E. in satellite communications from Kyushu University in 2002
and 2004. After joining NTT, he conducted
research on network management and QoE
issues. He was with NTT Communications from2012 to 2015. He is now working on the development of sophisticated operations of network systems by using AI. He is a member of the Instituteof Electronics, Information and Communication
Engineers (IEICE).

Yasuhiro Ikeda

Researcher, Traffic Engineering Group, Communication Traffic & Service Quality Project,
NTT Network Technology Laboratories.
He received a B.E. in applied physics andphysico-informatics and an M.E. in applied
physics from Keio University, Tokyo, in 2008
and 2010. Since joining NTT in 2010, he has
been researching traffic analysis techniques fortelecommunication networks. He received the
IEICE Information Network Research Award in2012. He is a member of IEICE.

Yuusuke Nakano

Research Engineer, Traffic Engineering Group,
Communication Traffic & Service Quality Project, NTT Network Technology Laboratories.
He received an M.E. in system engineeringfrom Wakayama University in 2005 and a Ph.D.
in information science and technology from
Osaka University in 2011. He joined NTT Network Service Systems Laboratories in 2005. He
is currently with NTT Network Technology
Laboratories. His research interests include network anomaly detection and web performancemeasurement and acceleration. He is a memberof IEICE and the Information Processing Societyof Japan.

7

Keisuke Ishibashi

Senior Research Engineer, Supervisor, NTT
Network Technology Laboratories.
He received a B.S. and M.S. in mathematics
from Tohoku University, Miyagi, in 1993 and1995 and a Ph.D. in information science andtechnology from the University of Tokyo in
2005. Since joining NTT in 1995, he has been
engaged in research on traffic issues in computercommunication networks. He received the Information Network Research Award in 2002. Since
2017, he has also been a visiting professor at
Osaka University. He is a member of the Instituteof Electrical and Electronics Engineers (IEEE),IEICE, and the Operations Research Society ofJapan (ORSJ).

Ryoichi Kawahara

Senior Research Engineer, Supervisor, Group
Leader, Traffic Engineering Group, Communication Traffic & Service Quality Project, NTT
Network Technology Laboratories*.
He received an M.E. in automatic control and a
Ph.D. in telecommunication engineering fromWaseda University, Tokyo, in 1992 and 2001.
Since joining NTT in 1992, he has been conducting research on traffic control for telecommunication networks, traffic measurement and analysis for Internet protocol networks, and network
management. He received the Telecom System
Technology Award from the Telecommunications Advancement Foundation in 2010, and Best
Paper Awards from IEICE in 2003 and 2009. He
is a member of IEICE, IEEE, and ORSJ.
*He left NTT at the end of March 2018.

Satoshi Suzuki

Senior Research Engineer, Access Network
Operation Project, NTT Access Network Service
Systems Laboratories.
He received an M.E. in global environmental
engineering from Kyoto University in 1995.
Since joining NTT in 1995, he has mainly been
researching and developing network operation
support systems in access and wide area Ethernet
networks. He is a member of IEICE.

NTT Technical Review

Feature Articles: Creating a Flexible and Smart
Network as a New Social Infrastructure

Initiatives toward Access Network
Technology for the Beyond-5G Era
Hiroyuki Furukawa, Takahito Kirihara,
Satoshi Narikawa, Katsuya Minami, Kenji Horikawa,
Satoshi Ikeda, and Takeshi Arai
Abstract

We have been researching future end-user lifestyles and business transformations for carriers and othertelecommunications providers that will be driven by changes in social contexts, particularly by changes
in mobile, video, and Internet of Things related markets. From these changes, we identified necessary
technologies to provide information and communication technology services that are expected to proliferate in the future. We present in this article our findings that examine future directions in the development of access network technology designed to realize the next-generation social infrastructure.
Keywords: access networks, future vision, advanced functionality

1. Social context changes driving
lifestyle transformations
Numerous changes are occurring in society as technology advances, and they are leading to varioustransformations in lifestyles. In this section, we
explore the background and expected outcome ofthese changes.
1.1 Background
Technological advances and changes in the social
context that have occurred and are expected to occur
between 2000 and 203x are illustrated in Fig. 1. The
time period from 2000 to the present has been theInternet technology growth period, as the number ofInternet users increased with the introduction of highspeed and large-capacity services. The fifth-generation (5G) service is anticipated to arrive in the mobile
market by 2020, which will boost transmission
speeds a hundred-fold. By 202x, the 5G service area
will have expanded, making 5G the foundational platform for information and communication technology(ICT) applications.
In the video content market, progress in practical
4K/8K applications is expected to heat up demand for1

retransmission of 4K/8K video over Internet protocol(IP) networks. In the worldwide Internet of Things(IoT) market, smartphones and tablets are the current
growth drivers, but in the coming years, various typesof IoT devices will be deployed such as smartwatches, smart glasses, and other wearables, as well as
numerous types of sensors and imaging devices suchas live cameras.
By 203x, we foresee enhanced broadband mobile
communications with an expanding coverage area
and the increasing use of multiple services. Additionally, the scope of the video content market will be
expanded to not only broadcast and video content but
also monitoring, remote medicine diagnosis, andother high-definition video applications. Furthermore, big data and artificial intelligence (AI) technologies will evolve and be used in more real-world
applications. This is expected to transform lifestyles
so that ICT services with multiple quality levels will
be accessible everywhere in the form of advanced
urban functions, which includes infrastructure, energy, traffic, and other areas.
1.2 Future ICT usage scenarios
Three ICT usage scenarios envisioned for 203x are
NTT Technical Review

Feature Articles

2000

2017~

105 million (100%)

88 million

MobileExternal

Commercial serviceof 4K/8K
Support for IP (VOD etc.)

IoT

3G

LTE

1 Mbit/s

DTB / Satellite broadcasting

150
Mbit/sLTE-A

Hundredsof Mbit/s

LTE-A
Over Higher speed
Pro 1 Gbit/s

DTB/SD/HD remasteredOver
4K/8K remastered 4K: 22ch/8K: 1ch (in 2018) 6 Gbit/s

Internet (OTT etc.): increasein video traffic

150 Tbit/s: accounts for
80% (in 2025)

Smartphones and tablets

Feature phones

770 million (2017)

560 million (100%) (2014)

B FLET’SADSLHigh speed
Large capacity

HIKARI
Collaboration 1 Gbit/s

5G: fifth-generation
AI: artificial intelligence
ADSL: asymmetric digital subscriber line
B2B2X: business to business to X
DTB: digital terrestrial broadcasting
FTTH: fiber to the home

HD television

Spread of HD
video market

Telemedicine

Monitoring
Self-driving

Spread of newvalue-added services Smart city
Providing new value
by using AI and big data

Smart factory

1.83 billion (327%) (2025)

Promotion of B2B2X

FTTH coverage ratio(Over 90%)

Internet growth (acceleration)

DTB of 4K

AI and data
analysis techniques

Image recognitionNGN

10 Gbit/s

Supports demands of various
users anywhere and anytime
via ICT services

Sensor devices andIoT terminals

Wearable devices

Number of terminals
Internal

5G+

Gbit/sHuge number ofvarious terminals
AI and big data

NTT Group

75 million (71%)

85 million
5G 1–10Realization of 5G
Increase in traffic

Video

203x~

202x

TOKYO
2020

Year

Working population

Slowing growth

PSTN migration
Smart equipment
Efficient operation

Wide area access network
Decrease of maintainers

HD: high definition
HIKARI Collaboration: NTT’s initiative for wholesale of fiber-optic services
ICT: information and communication technology
IoT: Internet of Things
IP: Internet protocol
LTE: Long-Term Evolution

LTE-A: LTE-Advanced
NGN: Next Generation Network
OTT: over-the-top
PSTN: public switched telephone network
SD: standard definition
VOD: video on demandFig. 1. Direction of ICT progress toward an abundant future society.

depicted in Fig. 2.
(1) Seamless connectivity
In the first scenario, users will be able to enjoy ICT
services at all kinds of locations without needing tothink about the access connection or device. As one
possible service in this scenario, a person talking on
the phone while walking down the street might decideto enter a coffee shop, where the call can be seamlessly continued as a video call on a display in the
shop. Realizing this usage scenario will require thefunctionality to continue communications seamlessly
even when the network connection points or devices
change, as well as the functionality to guarantee endto-end communication quality, which will maintainthe high-level user experience, from the server providing the service to the user’s device independent of
the transmission media (e.g., wireless or wired) or the
method of network connection.
(2) Advanced urban functions
In the second scenario, ICT will advance as urban
functions providing greater added value. One possible example is the use of dynamic maps, which will
be generated on servers based on a multitude of information sources from intelligent transport systems
Vol. 16 No. 6 June 2018such as sensors, video or camera images, and traffic
lights deployed throughout a city. The dynamic map
information will then be sent to cars and other vehicles to provide advanced onboard functions such as
hazard prediction. Implementing this usage scenario
will require the functionality to collect a lot of datafrom many sources in real time and calculate useful
information using networked computing resources,
and the functionality to distribute this informationfrom networked computing resources to large numbers of devices (cars) simultaneously and with low
latency.
(3) Flexible working arrangements
In the third scenario, we expect to see a need foradvanced ICT services to facilitate remote accessworking environments and flexible working arrangements, enabling people to work from home or from ashared office, for example. One possible working
arrangement involves colleagues in remote locations
carrying out group work via augmented reality conferencing available in each person’s home. This
working arrangement necessitates the assurance of
low-latency broadband communication paths between
multiple locations for the duration of the conference,2

Feature Articles

5G
Seamless connectivity

Wired

V2X
Wi-Fi

Advanced urban functions

Wired

5G

Flexible working arrangements

Wi-Fi

Bidirectional AR conferencing

Dynamic maps

50m

No concern aboutmeans of access(wired or wireless)

Advanced ICT
anywhere in the city

Advanced ICT services
available even in the home

AR: augmented reality
V2X: vehicle-to-everything

Fig. 2. Usage scenarios for high added-value public ICT services.

as well as advanced network authentication functionality that determines whether to permit the connection between multiple sites.
1.3 Trends for telecommunications carriers
We believe the telecommunications carriers of the
future will have to provide flexible network services
to both users and service operators. Flexible network
services involve three things: (1) customization that
permits users and applications to use only the bandwidth, latency, computing resources, and otherresources they need just when they need them; (2)instantaneous configurations and connections toenable instant real-time service usage; and (3) automatic selection of the optimum network without theuser having to choose one. Furthermore, to enable
users to conveniently access the services they subscribe to anywhere at any time, the capability fortelecommunications carriers to perform centralized,
end-to-end management is needed. Additionally, toprovide these services inexpensively and continuously, we believe further work-process automation
and reduction will be necessary through extensive
application of AI and robotic technologies in operations, administration, and maintenance. Automation
and reduction of work processes will also offset the
future decline in maintenance personnel precipitatedby a reduction in the working population in Japan.

2. Access network systems technologies for theera of 5G deployment
To realize the future vision described above, NTT
Access Network Service Systems Laboratories (ASLabs) aims to advance access network functionality
with four research and development (R&D) initiatives, as shown in Fig. 3. In addition to continuing
initiatives toward (1) realization of higher-speed
access networks, and (2) smart network facilities,
maintenance, and operations that utilize existing
facilities, we are pursuing R&D projects focusing on(3) the provision of optimal end-to-end quality that
goes beyond the scope of conventional quality management and also on (4) flexible and efficient deployment of network functions to address a variety ofcommunication quality requirements. These initiatives are outlined in the following subsections.
2.1 Realization of higher-speed access network
Our R&D efforts in the area of passive optical network (PON) architectures for the construction of lowcost optical access services have furthered the development of broadband-PON (B-PON) and gigabit
Ethernet-PON (GE-PON). We have also contributed
to the standardization of ITU-T*1 G.983 (2003) andIEEE*2 802.3ah (2004), which are for B-PON and*1 ITU-T: International Telecommunication Union - Telecommunication Standardization Sector*2 IEEE: Institute of Electrical and Electronics Engineers

3

NTT Technical Review

Feature Articles

Provision of optimal
end-to-end quality
Higher-speed
access networks

End-to-end network
management

PON systemwith enhanced
transmission capacity

Facilities for 5G mobile

Network manager

Automated
estimation of
failure points

Wide bandwidths

Core networks

Services for businesses

FASA

Inexpensive,high reliability

Flexible and efficient
deployment of
network functions

Stadium services High concentration

Smart network
facilities,
maintenance, andoperations

MMS

Cooperative
wireless LAN

Services for IoT
Narrow bandwidths,
high reliability

Services for IoT

Accessnetwork
quality controls

Services for consumers

Very inexpensive,wide coverage areas

Optimal quality for each service(4K/8K video, home automation,
HEMS, etc.)

FASA: Flexible Access System Architecture
HEMS: home energy management systems
MMS: mobile mapping system

Fig. 3. Image of future access networks.

GE-PON, respectively. In addition, we promoted thedevelopment of faster technologies that led first to thestandardization of 10-Gbit/s-capable Ethernet PON(10G-EPON, IEEE 802.3av, 2009) and later, of
40-Gbit/s-capable Next Generation-PON2 (NGPON2, ITU-T G.989, 2015) [1], which expanded
transmission capacities and improved extensibility
based on 10-Gbit/s-capable PON systems. These
high-speed access technologies are expected to be
crucial for the accommodation of mobile base stations for 5G and beyond, and for 4K/8K video content distribution. Current standardization efforts in
PON systems are focused on even higher speeds, anda standard for 100-Gbit/s-capable 100G-EPON [2] is
expected to be completed in 2019.
In the wireless access field, wireless local area networks (LANs) have achieved widespread proliferation, but the underlying standard, IEEE 802.11, wasfirst established back in 1997 when the maximum
transmission speed was just 2 Mbit/s. Demand forVol. 16 No. 6 June 2018higher wireless LAN speeds has continued to growsince this time, with the spread of laptops, tablets, andother wireless devices. To address this demand, R&D
progress has been made in orthogonal frequency division multiplexing and multiple-input and multipleoutput technologies, paving the way for the mostrecent iteration of the standard, IEEE 802.11ac,
which realizes physical speeds up to 6.9 Gbit/s. With
this new standard, transmission speeds have now
increased more than 1000 times in less than 20 years.
Further diversification of wireless devices is
expected to bring even better wireless environments.
One problem, however, is reduced throughput due toradio interference in highly concentrated usage environments such as sports stadiums. Standardization
efforts to improve throughput in such conditions are
now underway as IEEE 802.11ax. AS Labs is also
studying cooperative wireless LAN technology [3]that will improve frequency usage efficiency andachieve higher speeds.
4Feature Articles

2.2 S
 mart network facilities, maintenance, andoperations for access networks
To date, the main focus in access networks has been
on deploying network equipment and optical fiber
efficiently in order to deliver the optical fiber network
to homes. In the future, however, access network
facilities will have to cope with many diverse requirements to support the 5G and IoT telecommunications
infrastructures. For example, they will have to efficiently accommodate the increasing number ofmobile base stations or IoT devices over wide coverage areas. Consequently, we are looking into how to
optimize the deployment of access network facilities
while maximizing the use of existing ones.
Another area of focus involves achieving smarter
maintenance and operations for access networks. The
number of workers involved in maintenance andoperation of the network is expected to decrease in
the future. Therefore, it is necessary to be able tooperate and maintain the network with a smaller
number of workers by achieving smarter maintenanceand operations. For example, we are working to
improve the efficiency of equipment inspections byutilizing a mobile mapping system [4] and to reducethe burden of routine work in network operations orservice reception tasks and to simplify tasks that
require advanced skills by utilizing navigation technologies [5]. We are also striving to make networkfault detection operations more advanced and efficient using automatic failure points estimation technology [6] and to enable comprehensive management
and operation of diverse networks through the use of
network resource management technology [7].
In the coming years, we will pursue R&D ofadvanced automation of network operations by AI
and machine learning and remote maintenance using
sensors, robots, and other devices, while aggressively
systemizing routine operation tasks.
2.3 Provision of optimal end-to-end quality
Future networks will have to provide services with
optimal end-to-end communication quality from theservice provider network to the end user devices.
Networks will need the functionality to automaticallyguarantee required bandwidth for consumers just forthe duration a video service is used, for example, as
well as to permanently allocate a narrow bandwidthfor IoT devices. Access networks to date have generally provided best-effort transmission. However, weare researching functions that will recognize the services users are accessing and provide quality accordingly. As mentioned previously, communications do
5

not exclusively use NTT networks but pass throughthe networks of other communication carriers andultimately traverse the user’s own network. A requirement in providing end-to-end quality, then, is to coordinate our networks with these other networks. Consequently, we are studying network technologies andoperation systems technologies that enable quality
coordination and cooperation between networks.
2.4 F
 lexible and efficient deployment of network
functions
Network functions virtualization, a network architecture concept involving the virtualization of manydifferent network node functions in core networks, isgradually being rolled out. In contrast, access network systems development has been conducted separately for each provided service and function, which
has stymied the deployment of generic network functions.
AS Labs, therefore, is engaged in researching Flexible Access System Architecture (FASA) [8], which
segments the functions provided by access networks
into components and allows the flexible combinationof those components. For example, FASA turns bandwidth management functions, maintenance functions, and multicast processing functions into (software) components, making it possible to add on
functions just for users that need that function or
combination of functions. This, in turn, is anticipatedto facilitate not only rapid provision of a variety ofservices but also reductions in capital expenditures
through the use of general-purpose hardware, reductions in operating expenses by communalizing maintenance operations, and service continuity assurance
by eliminating equipment end-of-life issues with theuse of general-purpose hardware.
AS Labs has issued a FASA white paper describing
a common application programming interface
enabling the addition and replacement of software
components and has proposed its standardization at
the Broadband Forum (BBF). We are also active inthe open implementation of the FASA platform incooperation with the Open Networking Foundation(ONF) and other open-source software communities.
3. Future development
In addition to our previous studies on technology
for high-speed and high-capacity networks, AS Labs
is pushing forward with R&D in advanced accessnetwork functionality. We are also studying how toprovide optimal end-to-end quality and flexible
NTT Technical Review

Feature Articles

deployment of network functions, as well as investigating efficient base-station accommodation and new
cabling applications.

[5]

References
[1]
[2]
[3]

[4]

Recommendation ITU-T G.989: “40-Gigabit-capable passive optical
networks (NG-PON2): Definitions, abbreviations and acronyms,”
2015.
IEEE 802.3ca, http://www.ieee802.org/3/ca/
K. Kawamura, K. Ishihara, J. Iwatani, S. Shinohara, M. Akimoto, Y.
Inoue, T. Ichikawa, and Y. Takatori, “New Trends in Wireless LAN
and Cooperative Wireless LAN Technology,” NTT Technical Review,Vol. 15, No. 3, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201703fa3.html
K. Sasaki, “Research and Development of Innovative Operation Technology for Access Network Infrastructure,” NTT Technical Review,Vol. 16 No. 6 June 2018

[6]

[7]

[8]

Vol. 15, No. 4, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201704fa4.html
H. Harada, “Operations Technologies to Improve Work Efficiency andCreate Value,” NTT Technical Review, Vol. 14, No. 4, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201604fa5.html
K. Itoi, T. Yakawa, H. Oishi, and K. Okazaki, “Automatic Failure
Points Estimation Technology for Generating Knowledge and Rapid
Recovery Operations,” NTT Technical Journal, Vol. 29, No. 5, pp.
60–64, 2017 (in Japanese).
S. Horiuchi, K. Akashi, M. Sato, and T. Kotani, “Network Resource
Management Technology,” NTT Technical Review, Vol. 15, No. 10,2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201710ra2.html
A. Otaka, “Flexible Access System Architecture: FASA,” NTT Technical Review, Vol. 15, No. 4, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201704fa5.html

6Feature Articles

Hiroyuki Furukawa

Senior Research Engineer, Access Network
Service Systems Project, NTT Access Network
Service Systems Laboratories.
He received a B.E. in electrical and electronicengineering and an M.E. in information engineering from Nagoya University in 1997 and1999. He joined NTT in 1999. He is currently
engaged in R&D focused on creating novel
access network services.

Takahito Kirihara

Kenji Horikawa

Senior Research Engineer, Access Network
Service Systems Project, NTT Access Network
Service Systems Laboratories.
He received a B.E. and M.E. in electrical andelectronic engineering from Tokyo Institute ofTechnology in 1992 and 1994. He joined NTT in
1994, where he has been engaged in R&D ofcomputer networks. He is a member of IEICE.

Satoshi Ikeda

Senior Research Engineer, Access Network
Service Systems Project, NTT Access Network
Service Systems Laboratories.
He received a B.E. in electronic engineeringand an M.E. in information science and technology from Hokkaido University in 2004 and 2006.
He joined NTT Access Network Service Systems
Laboratories in 2006, where he has been
researching innovative network technologies foraccess applications. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE).

Senior Research Engineer, Supervisor, AccessNetwork Service Systems Project, NTT AccessNetwork Service Systems Laboratories.
He received a B.E. and M.E. in electrical communications engineering from Tohoku University, Miyagi, in 1993 and 1995. He joined NTT in
1995 and began researching multi-channel video
signal transmission systems for FTTH (fiber-tothe-home). He is currently engaged in R&D of
novel access network services. He is a member of
IEICE.

Satoshi Narikawa

Takeshi Arai

Senior Research Engineer, Access Network
Service Systems Project, NTT Access Network
Service Systems Laboratories.
He received a B.E., M.E., and Ph.D. in electrical and electronic engineering from Tokyo Institute of Technology in 2001, 2003, and 2012. He
joined NTT Access Network Service Systems
Laboratories in 2003, where he has been
researching innovative network technologies foraccess applications. He received the Young Engineer’s Award from IEICE in 2007 and the Best
Paper Award from IEICE in 2008. He is a member of IEICE.

General Manager, Access Network Service
Systems Project, NTT Access Network Service
Systems Laboratories.
He received a B.E. and M.E. in instrumentationengineering from Keio University, Kanagawa, in1991 and 1993. He joined NTT in 1993. He iscurrently engaged in R&D focused on the creation of novel access network services.

Katsuya Minami

Senior Research Engineer, Access Network
Service Systems Project, NTT Access Network
Service Systems Laboratories.
He received a B.E., M.E., and Ph.D. in information systems engineering from Osaka University in 1998, 2000, and 2003. He joined NTT in
2003. He is currently researching and developing
novel access network services. He is a member of
IEICE.

7

NTT Technical Review

Feature Articles: Creating a Flexible and Smart
Network as a New Social Infrastructure

Research toward Realizing a Future
Network Architecture
Seisho Yasukawa, Hiroaki Sato, Takeshi Hirota,
Takuya Tojo, Ken-ichi Endo, Yasunobu Kasahara,and Hiroshi Suzuki
Abstract

In the 5G (fifth-generation mobile communications) and Internet of Things era, trends such as workon social infrastructure services, the explosive increase in the number of terminals and the amount of
network traffic, and the use of artificial intelligence for smart devices and services will have a huge
impact on networks, thereby increasing the need for new network architectures. This article analyzes theimpact of these trends, examines network requirements, and introduces initiatives to develop the necessary architectures and key technological elements verified by proof of concept.
Keywords: network architecture, 5G, PoC

1. Five elemental technologies
The NTT laboratories are studying network infrastructure architectures and practical elemental technologies that will provide B2B2X (business-to-business-to-X) model business and other societal infrastructures in the future as we fully enter the fifthgeneration mobile communications and Internet ofThings (5G/IoT) era.
This article reports on the following five elemental
technologies.
(1)	Network slice technology, which provides 5Gtransport able to accommodate multiple societal infrastructures such as self-driving andremote factory control and new digital services such as IoT. It can isolate multiple logicalservice networks for operators with different
characteristics.
(2)	Cloud native software-defined anything(SDx)* control technology, which enables
cloud operators to create their own services bytuning and linking cloud applications and network applications from a simple catalog ofapplications on a self-service basis.
(3)	Multi-layer software-defined networking
1(SDN) control technology, which provides
integrated control of Internet protocol (IP) andtransport layers using an SDN controller, for
rapid service provision and resource optimization over the entire network.
(4)	Content delivery network (CDN) technology,
which is important for realizing 5G transport
and will provide economical, high-quality
distribution of high-definition, realistic video
content including 4K/8K, augmented reality(AR), and virtual reality (VR) content.
(5)	Inter-network cooperative mechanisms forprotecting systems against massive cyberattacks. The mechanisms deal with increasingly
large-scale and diverse distributed denial ofservice (DDoS) attacks by obtaining andspreading security threat information among
network operators beforehand as a preventive
defense against attacks. These mechanisms
also link multiple network security functions
to implement stronger detection and prevention functions.
*

SDx: A general term for technology to control information technology infrastructure resources (servers, storage, networks, etc.)
via software.

NTT Technical Review

Feature Articles

APP

Content service
providers
IoT service
providers

Central control

Cache
Virtual network

APP

Central control
Virtual network
APP
APP
APP

MVNO/ISPs
Virtual network
Provides networks to combine suitablevirtual networks for higher-layer providers
LowCloud connection latency
cloud

Ultra-reliable low-latency communication virtual network
Session control

Cloud connection

Cloud

Massive machine-type communication virtual network
Packet transfer

Internetconnection

Packet transfer

Internet

Enhanced broadband virtual network
Physical infrastructure

Configures
virtual networks to combine virtual resources
depending on communication requirements
Transport

Fiber

ServerTransport

ServerAccessWireless

Transport

Transport
LAN

BackboneServerTransport
LAN

APP: application
ISP: Internet service provider
LAN: local area network
MVNO: mobile virtual network operator

Fig. 1. Network slice concept.

2. Network slice technology
Network slicing is a major new and innovative 5G
era network technology, along with high-capacity
packet transport and low-latency datacenter connection technologies. Network slicing involves managing physical equipment (physical resources) such asservers and routers as resources that can be partitioned virtually (virtual servers, virtual links, virtual
network functions, etc.). This technology makes it
possible to configure virtual networks (slices) by
combining these virtual resources on the shared
physical equipment (Fig. 1). A strength of network
slicing, unlike conventional virtual private networks(VPN) and virtual routers, is that programmably controlled end-to-end networks can be configured immeVol. 16 No. 6 June 2018diately, like a cloud service, by flexibly combining
virtual lines, virtual servers, virtual network functions, virtual high-order application functions, andvirtual operations support systems and business support systems (OSS/BSS). The slice user is able tofreely select network functions and control protocols
and to control routes without being restricted byphysical network functions, hierarchical structures,or operational rules.
As extreme examples, an IoT service provider
could operate a network without using Ethernet or IP,
or a content service provider could use its own routing control protocols and its own quality of service(QoS) policies that do not conform to international
standards.
Network slicing is useful, for example, for achieving
2

Feature Articles

a variety of communication conditions on the same
physical equipment, as with 5G networks, or for providing program-controllable virtual resources tohigher-layer service providers.
The international standards for 5G cover three communication modes for implementing various communication conditions: high-capacity broadband communication (e.g., 4K/8K video distribution), massive
machine-type (many session/connection) communication (e.g., IoT), and ultra-reliable low-latency communication (e.g., for AR, self-driving cars); and weare studying ways to configure virtual networks thatrealize each of these (Fig. 1).
For example, most of the traffic in a high-capacity
broadband communications network would be transferred via the Internet, so deploying a packet transport function with a tree structure and the Internet
connection as the root would be efficient. For a many
session/connection communication network, it would
be effective to deploy many session/connection functions at the location where the sessions are needed.
For an ultra-reliable low-latency communication network, deploying an ultralow-latency cloud near theaccess location would be effective for connecting to adatacenter within a range that meets latency requirements.
Three types of services for providing virtual
resources to higher-layer service providers are being
considered. They are: network slicing as a service(NSaaS), which provides an end-to-end virtual network with a full set of functions that can be oriented,
for example, to Internet service providers (ISPs) andmobile virtual network operators (MVNO); network
slicing platform as a service (NPaaS), which provides
virtual network platforms that can be combined andcustomized by higher-layer service providers; andnetwork slicing infrastructure as a service (NIaaS),
which provides individual components such as virtual servers and virtual links.
For example, an MVNO or ISP could use NSaaS to
procure a full set of virtual communications-operator
facilities, including virtual OSS and BSS, and thenconduct business using only its own subscriber web
user interface. Or, as live events are held in cities likeTokyo, Nagoya, and Sapporo on a daily or weekly
basis, a content provider could use NPaaS to move its
own live video processing and distribution functions
to buildings near the venues and configure virtual
CDNs on a daily or weekly basis to satisfy latency
requirements. The NTT laboratories are working on
international standardization and research and development (R&D) on network slicing technologies
3

including slice management, slice gateways, slice
isolation, and telemetry, aiming for commercial
implementations in 202x.
2.1 Slice management technology
Slice management technology is being modeled inthree layers, for the infrastructure provider, slice provider, and slice operator (higher-layer service provider), and application programming interfaces(APIs) between these layers are being studied(Fig. 2).
The infrastructure provider owns the network anddatacenter facilities and manages them as pools of
resources such as virtual links (bandwidth) and virtual servers (central processing unit (CPU) capacity).
A slice provider procures virtual resources from theinfrastructure provider through an API and configures slices. In practical operation, this three-layer
model is not limited to simple, horizontal specialization, and APIs are created taking all kinds of realconfigurations into consideration. For example, aslice operator might combine virtual resources fromits own facilities with virtual resources from otherfacilities and control programs centrally without
regard for whose facility is being used.
We are also studying ways to apply telemetry technology to programmable slice monitoring, control,and collection of analysis data, in units of slices, fromAPIs for slice operators.
2.2 Slice gateway technology
Slice gateway technology is used for connecting
slices among slice providers and for connecting subslices within a slice provider. We are studying technologies to link slice management functionality withslice access authentication functionality and packet
transport functions, to allow packets to be transported
to appropriate slices (or subslices) based on slice connection policies. It also provides the edge functionality for slice isolation, as described next.
2.3 Slice isolation technology
Slice isolation involves isolating end-to-end traffic
flow among slices, over link segments, including traffic within physical servers. The future goal is toachieve complete noninterference among slices sothey will not affect each other, regardless of traffic
congestion, functional faults, or software bugs. However, this is difficult to achieve with the current virtualization technology, so we are studying implementations ranging from loose isolation, on the level ofcontrolling QoS priorities, to strict isolation, which
NTT Technical Review

Feature Articles

Higher-layer service providers
VirtualOSS/
BSSHigher-layer service providers

Higher-layer service providers

Serviceprovider’s
APPs

VirtualOSS/
BSSServiceprovider’s
APPs

Virtual network
functionsAPI
Slice management

Slice management

End-to-end slices
A full-set of end-to-end virtual
networks and OSS/BSS(e.g., MVNO or ISP)

Access subnetslices(e.g., mobile,
Wi-Fi, or fixed)

Slice providers(e.g., NSaaS)

Slice management

Core subnetslices(e.g., cloud)Core subnetslices(e.g., backbone
or CDN)

Slice
gateways

Virtualmachines

Core subnetslice(e.g., MEC)

Virtual links
Slice providers(e.g., NIaaS)

Slice providers(e.g., NPaaS)

API

Virtual resource
management

Virtual resource
management

Telemetering

Telemetering

Physical networks and servers(fiber-optic line, wireless line, etc.)

Physical servers

Infrastructure providers(e.g., access providers or ISPs)

Infrastructure providers(e.g., cloud providers)

MEC: multi-access edge computing

Fig. 2. Network slice model.

maintains a higher degree of noninterference by
securing resources such as virtual link bandwidth andvirtual server CPU. For virtual link protocols in particular, there are international standard protocols thatcan separate multiple flows, for example, VXLAN(Virtual eXtensible Local Area Network), MPLS
(Multi-Protocol Label Switching), and segment routing (SR). The NTT laboratories are working to augment these protocols to incorporate requirements for
isolation and create new international standards.
3. Cloud native SDx control technology
Cloud native SDx control provides services easily
and rapidly using automatic end-to-end control for
network and cloud environments. It also offers theapplications needed to provide services, rather thanjust providing individual network services (Fig. 3).
Conventional network services were simply functions that connect service users and service providers.
Vol. 16 No. 6 June 2018

Service providers are now providing diverse services
on cloud environments, but the inability of cloud platforms to coordinate with networks is an obstacle toproviding services quickly. As such, we aim toachieve rapid provision of services by collectively
controlling networks, cloud environments used byservice providers, and even applications. Implementing this concept requires mechanisms to automatically configure resources for providing services(networks, cloud resources, etc.), and to manage theinformation needed to support the resources.
We are studying ways to provide these automatedmechanisms as user-friendly service configuration
patterns that service providers can customize according to their own services, based on various technologies such as cloud environment workflows, cloudenvironment control, and network control. Automatic
control of these various resources also requires
appropriate management of information, such as
what resources are being controlled, the state of each4Feature Articles

Service providers can customize
resource information and control
order according to their service
based on the service
configuration pattern.

Service provider

APP

APP

APP

APP

Automatic control infrastructure
Configuration
andstatus information

Workflow

APP

APP

XX service
YY service
Xx service

APP

APP
APP

APP

Cloud infrastructure
Private cloud

Cloud infrastructure
Edge cloud

VPN

VPN

Best effort

Best effort

Leased line

Leased line
Accessnetwork
End user

APP

APP

APP

APP

Core network

Cloud infrastructure

Cloud infrastructure

Edge cloudPublic cloudFig. 3. Cloud native SDx control technology.

resource, and in what order settings need to be
applied. Consequently, we have been studying models for entities being controlled and ways to manage
overall configuration and state information, so thatthe various entities being controlled can be handled
uniformly, rather than being dependent on particularservices or physical equipment.
By establishing methods for the modeling and configuration management that we are currently studying, and building them into an automated control
mechanism, we will realize a control platform able tocentrally manage the resources needed for services
and to control them automatically.
4. Multi-layer SDN control technology
We are studying multi-layer SDN control technology in order to achieve rapid service provision andoverall optimization of network resources. Multilayer SDN control integrates the control of IP andtransport layers from the SDN controller, enabling
the settings needed in routers and transport equip5

ment to be done simultaneously, so that paths fordifferent types of services such as IP-VPN or dedicated Ethernet lines can be provided on-demand fromthe SDN controller (Fig. 4). In addition to working toachieve rapid provision of services in this way, we arealso studying ways to provide various grades of quality and reliability, in anticipation of the various levelsof service that will be required in the 5G era [1].
Specifically, we are studying (1) implementation ofrouting control and protection in the IP layer with SR
suitable for SDN control, (2) implementation of routing control and restoration in the transport layer with
optical wavelength switching, (3) real-time monitoring of the network state using streaming telemetry
technology, and (4) optical wavelength defragmentation to reorganize fragmented optical wavelengths(Fig. 5). By combining these elemental technologies,
we aim to achieve graded quality and reliability, an
SDN controller that can control IP and transport layers autonomously according to network conditions,and new network operations that will maximize theutilization of all network resources. We are currently
NTT Technical Review

Feature Articles

Virtual path #1 (Financial company)
Datacenter
Financial company(Low latency)

IoT company(Best effort)

Datacenter

1 Gbit/s

ServicedeliveryRequest

Dedicated path
1 Gbit/s

Virtual path #2 (IoT company)
Shared path
10 Mbit/s

SDN controller

Datacenter

SensorIntegrated control

RouterIP layer

RouterTransport
layer

RouterRouter

OXC

Router

OXC
OXC

OXC
OXC

Maximize network
resources ineach layer.
OXC

OXC

OXC: optical cross connect

Fig. 4. Multi-layer SDN control technology.

implementing prototypes of the controller functions
shown in Fig. 5, based on an open-source SDN controller called ONOS (Open Network Operating System) and verifying the technology as we work toestablish multi-layer control technology.
5. CDN technology
We are studying CDN technologies for high quality, economical distribution of high-definition, highly
realistic next-generation video content, including
4K/8K and AR/VR (Fig. 6). CDN is based on three
key technologies: video quality of experience (QoE)
control and distribution design technology, real-timehigh-capacity distribution technology, and state visualization technology.
5.1 V ideo QoE control and distribution design
technology
Video QoE control and distribution design technology is designed to implement video content distribution economically, based on QoE. It will be able topreserve QoE while achieving efficient utilization offacilities by optimizing the balance between QoE andVol. 16 No. 6 June 2018

facility resources. It will use information measured
from the various equipment, applications, and terminals on the network to estimate QoE, viewing conditions, and the states of servers, networks, and otherresources. It will select distribution servers and routes(server/content navigation) to control traffic according to factors such as the viewer’s subscribed services, and perform content cache placement linked withthis control.
5.2 R
 eal-time high-capacity distribution technology
Real-time high-capacity distribution technology
realizes efficient and stable, high-capacity live video
distribution. Normally, HTTP (Hypertext Transfer
Protocol) based unicast communication is used
between distribution servers and terminals, so whenviewer demand increases due to events such as live
coverage, the load on distribution servers and the
network increases greatly. Converting distribution
over the network segments to multicast and leaving
distribution server and terminal communication as
unicast (unicast/multicast conversion) makes it possible to avoid multiple distributions of the same live6

Feature Articles

User
ServicedeliveryRequestSDN controller
APP

Virtual path QoS management
Virtual path management
Request info.

Core

Wavelength defragmentation

Virtual path allocation

Path info.

Device/link management

IP route
configuration

L3/L2VPN configuration

Wavelength route
configuration

Protection/restoration configuration

Topology

Link

DeviceWavelength

Device configuration (YANG) / Route configuration (PCE)
Configuration protocol(NETCONF, REST)

SBI

Telemetry protocol(gRPC)

Integrated control
IP layer

Optical layergRPC: An open source remote procedure call (RPC) system.
NETCONF: Network Configuration Protocol
PCE: Path Computation Element

REST: Representational State Transfer
SBI: southbound interface
YANG: Yet Another Next Generation

Fig. 5. Elements of multi-layer SDN control technology.

Content distributor

CDN analysis report

Policy control/
video manifest
serverNetwork status/
QoE analysis server
QoE log

Cache

QoE control

Rate control
Cache

Originserver

CDN

Live delivery edge

Live delivery edge
NetworkMC

UC

UC
Live theater

Real-time large capacity
distribution (UC/MC conversion)
QoE: quality of experience
UC/MC: unicast/multicast

Fig. 6. CDN technology.

7

NTT Technical Review

Feature Articles

External CTI sources(e.g., CSIRTs, ISACs)
Proactively prevent cyberattacks by collecting
CTIs from multiple networks and externalsources, analyzing them comprehensively,
and sharing the results.

Mitigation
Cyber threat
intelligence

NetworkD
Mitigation

Attacker
Cooperation

Collection, analysis, sharing

Analysis
Detection
Protect services against
massive cyberattacks throughcooperation of security
functions in different networks.

Network
B

Blacklist

NetworkC
Analysis

C&C server
Detection

Cooperation
Network A

Isolation

Detect and isolate infected devices
on basis of network-specificinformation from other networks.

OK

Security service
subscriber

Infecteddevices

Web servers

C&C: command and control
CSIRTs: computer security incident response teams

Normaldevices

Security service
subscriber

CTI: cyber threat intelligence
ISAC: Information Sharing and Analysis Center

Fig. 7. Cooperative mechanisms for protecting systems against massive cyberattacks.

video and also increases the utilization efficiency of
distributor servers and other facilities, while stillachieving stable distribution.
5.3 State visualization technology
State visualization technology is designed to estimate and visualize metrics such as distribution conditions, QoE, and viewing behavior (playback, pause,
seek, termination of video) from information measured and accumulated from devices, applications,and terminals on the network, and to provide thisinformation to content distributors. This enables content distributors to understand the characteristics ofcontent viewing by viewers, helping them to improve
efficiency.
We hope to implement a high-performance, economical CDN platform using these technologies,
carrier network platforms, and the accompanying
management information.
6. Cooperative mechanisms for protecting
against massive cyberattacks
With recent increases in the scale of DDoS attacks
Vol. 16 No. 6 June 2018

and diversification in the types of malware, it is
becoming difficult for individual networks to deal
with such attacks efficiently in terms of performanceand functionality. To overcome this, it is important tostrengthen detection and protection functions by
obtaining and sharing security threat information
among network operators as preventative attack
countermeasures and to link the security functions
among multiple networks. Therefore, efforts areunderway to realize advanced preventative defense
capabilities, increased capacity of DDoS attack
defenses, and advanced detection and prevention
mechanisms for malware infection (Fig. 7).
6.1 Advanced proactive defense
For advanced proactive defense, we are studying
mechanisms that generate and share network-aided
cyber threat intelligence (CTI) faster and more accurately through comprehensive analysis of network
flows and other network-specific information
between multiple networks as well as collecting CTI
from external sources. These mechanisms will contribute to advancing proactive defense techniquessuch as expanding domain name system (DNS)
8

Feature Articles

blacklist information.
6.2 Increased capacity of DDoS attack defenses
To increase the capacity of DDoS attack defenses,
we are studying a mechanism to distribute DDoS
attack traffic to the defense functions of networks on
the attack route while considering the defense capacity of each network. This is expected to achieve
appropriate cooperation of defense functions.
This mechanism has enabled us to defend against
attacks of dramatically higher bandwidth than singlenetwork countermeasures.
6.3 Advanced detection and prevention mechanisms for malware infection
To achieve more advanced malware infection
detection and prevention mechanisms, infected
devices must be rapidly and accurately detected andisolated to prevent information leaks and other consequences. However, it is difficult to isolate only theinfected devices on each network when the DNS
server that can detect the typical behavior of malware(i.e., a C&C (command and control) server) and thedevices are on different networks. Therefore, we are
studying a mechanism to link the detection information in the DNS server and the device information, inorder to quickly and accurately isolate the infected9devices.
This initiative aims to achieve a mechanism todetect and protect against cyberattacks by linking
functions and information of multiple networks. In
the future, we plan to evaluate the technology oncommercial networks, identify issues toward practical implementation, and promote system implementation.
7. Future prospects
We have described the results of studying future
network technologies, including network slicing,cloud-native SDx, multi-layer SDN control, CDN,
and inter-network cooperative mechanisms for protecting systems against the expanding and diversifying cyberattacks. We will continue to demonstratetechnologies with proof of concept systems, moveelemental technologies toward completeness, andrefine the overall architectures.
Reference[1]

T. Tojo, T. Matsukawa. S. Okada, S. Arai, and S. Yasukawa, “Multilevel Reliability Architecture for Network Slicing in Metro Networks,” Proc. of the 23rd IEEE International Symoposium on Local
and Metropolitan Area Networks (IEEE LANMAN 2017), Osaka,
Japan, June 2017.

NTT Technical Review

Feature Articles

Seisho Yasukawa

Group Leader, Senior Research Engineer, NTT
Network Technology Laboratories.
He received a B.E. and M.E. in applied physics
from the University of Tokyo in 1993 and 1995.
Since joining NTT in 1995, he has conducted
R&D of asynchronous transfer mode (ATM)based multimedia switching and NGN (NextGeneration Network) architecture. He has also
been engaged in standardization activities related
to P2MP (Point-to-Multipoint)-MPLS. His current research involves 5G transport technology.

Hiroaki Sato

Senior Research Engineer, NTT Network Service Systems Laboratories.
He joined NTT in 1992 after receiving a master’s degree from Tokyo Institute of Technology.
He developed asynchronous transfer mode passive optical network (ATM-PON) systems, IP
multicast CDN systems, and IPv6 backbone
systems for Internet access. He specified thevideo streaming guideline on LTE (Long Term
Evolution) for content providers and developed
SDN-LAN to connect network functions virtualization systems including vEPC (virtual evolved
packet core) in NTT DOCOMO. Since returning
to NTT, he has been investigating vCPE (virtualcustomer premises equipment) using open source
software and 5G transport with network slicing.

Takeshi Hirota

Senior Research Engineer, Network Architecture Innovation Project, NTT Network Technology Laboratories.
He has worked on the development of a network management system for synchronous digital hierarchy, ATM, and packet transport technology. He is currently researching automatic unified control from networks to cloud environments. He is a member of the Institute of Electronics, Information and Communication Engineers (IEICE).

Ken-ichi Endo

Senior Research Engineer, Network Architecture Innovation Project, NTT Network Technology Laboratories.
He received a B.E. and M.S. in electronic engineering in 1984 and 1986. He joined NTT LSILaboratories in 1986. He has been with NTT
Network Technology Laboratories since 2012,
where he is studying distribution of next-generation video content.

Yasunobu Kasahara

Senior Research Engineer, Access Network
Service Innovation Group, Access Network Service Systems Project, NTT Access Network
Service Systems Laboratories.
He received a B.E. and M.E. in electronicengineering from Nihon University, Chiba, in
1996 and 1998. He joined NTT in 1998 andworked on the development of optical accesssystems such as ATM systems, passive optical
networks, and layer 2 switches. He is currently
engaged in R&D of content distribution technologies for emerging services. He is a member of
IEICE.

Hiroshi Suzuki

Researcher, Transport Service Systems Development Project, NTT Network Service Systems
Laboratories.
He received a B.E. and M.E. from Shizuoka
University in 2008 and 2011. He joined the NTT
Group in 2011. His recent research involves
cooperative mechanisms for protecting against
massive cyberattacks.

Takuya Tojo

Senior Research Engineer, Network Architecture Innovation Project, NTT Network Technology Laboratories.
He received a B.E. and M.S. in computers andsystems engineering in 2002 and 2004, and a
Ph.D. in advanced science and technology in2007 from Tokyo Denki University. He joined
NTT Service Integration Laboratories in 2004.
From 2012 to 2014, he was with NTT DOCOMO. He is a member of IEICE.

Vol. 16 No. 6 June 201810

Feature Articles: Creating a Flexible and Smart
Network as a New Social Infrastructure

MOOSIA: Technology for One-stop
Operation
Takaaki Moriya, Atsushi Yoshida, Yoshihiko Ito,
Masahiro Kobayashi, Shigeaki Harada,and Shingo Horiuchi
Abstract

This article introduces MOOSIA (Multi-Orchestrator for One-Stop operation with Innovative Aggregation of services), NTT’s orchestrator that enables service providers to combine, operate, and maintainnetwork, cloud, and application services in a unified manner.
Keywords: B2B2X, service collaboration, operation

1. Introduction
The flourishing application programming interface(API) economy and the cloud-first movement motivated us to research and develop capabilities for onestop operation that will enable service providers to
combine and operate networks, clouds, and applications provided by multiple operators, in a unified
manner. We have also put forward the concept of
MOOSIATM*1 that is aimed at achieving a world thatmakes it easier to put exciting ideas into practice, that
is, a world that makes it easier than ever to create,provide, and operate services proposed or planned byenterprises or individuals.
In the business-to-business-to-X (B2B2X) model,
companies representing the second B (i.e., service
providers) can freely and flexibly combine wholesale
services such as networks, clouds, and applications,which are provided by first-B companies (i.e., carriers) in order to provide a collaborative service(referred to from here on simply as service unlessotherwise specified) to end users (the ‘X’ in B2B2X)
as shown in Fig. 1.
In this article, we describe (1) zero-touch network
operation and closed-loop technology that evolves
operations mainly within first-B companies, (2)application of artificial intelligence (AI) technologies
to closed loops, (3) the API orchestrator that is the1core of service creation by second-B companies, (4)technology for enhancing the API orchestrator, (5)the API framework that functions as a general system
for API specifications, and (6) the future evolution of
MOOSIA.
2. Zero-touch network operation andclosed-loop technology
Current first-B operators such as large-scale network operators are feeling the pressure of a large
workload and heavy use of human resources just to
keep their networks functioning 24 hours a day, 365
days a year. It is crucial to reduce the operational
workload and shift the work done at night and on
weekends to daytime, weekday work in order toreduce operating expenses. Networks are currently
operated manually, with some minimal help provided
by operations support systems. Skilled engineers are
required for decision making and network control to
implement recovery when failures occur, and therefore, 24/7 emergency response teams are required,further increasing operation expenses.
Zero-touch network operation is aimed at solving*1 MOOSIA: An acronym for Multi-Orchestrator for One-Stop operation with Innovative Aggregation of services. It is a trademarkof NTT.

NTT Technical Review

Feature Articles

2nd loop
Phase 1:
Launching a newservicePhase 3:
Improving theserviceEnd users
New service(collaborative service)

Phase 2:
Operating theserviceSecond B (service providers)
Bulk ordering

Supports each operation phase of second-B
operators (service providers)

Bulk monitoring
API framework

Common APIs

API orchestrator

Adapter
Network-AI

Closed loop
(1st loop)

API

Decisionmaking

API

API

API

Control

Information
management

Integrated control

Data
platform

Servers

Network resource
management technology

Network

Network operator

Servers
Network
Network operator

Cloud
operator

Cloud operator(non-standard API)

Wholesale services of first-B companies

Catalog

Network-AI: NTT’s artificial intelligence technology for networks.

Fig. 1. Overview of MOOSIA in B2B2X model.

this problem by automating network control. Autonomous network operation, along with the control loopthat enables it, is important in achieving this goal, as
shown at the bottom left of Fig. 1. This loop involves
the following sequence of steps: (1) collect and manage information on the operation status of wholesale
services, → (2) analyze the collected information andmake decisions, → (3) control wholesale services, →return to (1) and repeat. This process is called aclosed loop as defined by TM Forum*2 (TMF), which
is a standardization association related to network
operations.
Achieving such a closed loop requires unified network resource information between the collection,
decision making, and controlling processes. We haveproposed network resource management technology
for this purpose. This technology is equipped withconfiguration and management functions based on
generic management targets called entities. In addiVol. 16 No. 6 June 2018

tion, this technology provides an external mechanism
for prescribing the characteristics of individual unitsof equipment and communication protocols as a
specification, which is separate from the programs
making up the operations support system. This technology also features functions for specifying interlayer relationships together with these specifications
to achieve integrated network management [1].
We are applying this network resource management technology to develop a data collection andusage platform that unifies network configuration
data based on a uniform model. This platform collects
and stores in common a wide range of data such as
network configuration data, alarms, and logs from
*2 TM Forum (formerly known as the TeleManagement Forum): A
global standardization association in the telecommunications industry that discusses and specifies management technologies andstandardization in relation to communications of the future.

2

Feature Articles

individual pieces of equipment. The platform transfers the data to Network-AI (NTT’s AI for networks).
In addition, integrated control within a first-B operator consists of technology that enables unified management of the virtual and non-virtual network. With
integrated control, the above network resource management technology is used to manage network
resources and enable uniform orchestration of theexisting non-virtual network and the virtualized network (i.e., software-defined networking).
3. Application of AI technologies to MOOSIATo implement a closed loop, an advanced intelligent process consisting of analysis, prediction, anddecision-making must be incorporated into the system. Incorporating the AI technologies described in
the article “Creating New Value by Leveraging Network-AI Technology in Service Operations” [2]enables MOOSIA to analyze the massive amount of
information accumulated in the data collection andusage platform in order to promptly detect risks suchas equipment failure and traffic congestion that might
affect service quality. Similarly, when building a service for a second-B company or changing its configuration after a service launch, or when carrying out aprimary response to an equipment failure or traffic
congestion, MOOSIA immediately computes an optimal plan for reconfiguring the service among thehuge number of combinations available based on
network configuration and resource conditions. This
optimal plan guarantees performance while minimizing cost. Then MOOSIA forwards the plan to theintegrated control mechanism.
As the first step in applying AI technologies to
MOOSIA, we are holding a closed-loop trial applying optimal resource allocation technology that considers future demand in virtual networks [3].
4. API orchestrator
The technologies described above pertain mainly tothe evolution of wholesale services within a network
operator. The following issues arise when multiple
wholesale services of various first-B companies are
combined to enable second-B companies to provide
new collaborative services in an end-to-end fashion:(1) compatibility between different wholesale services must be taken into account when combining
them; (2) the cause of a fault in a collaborative service
must be searched out by carrying out troubleshootingthat spans multiple wholesale services; and (3) auto3

mating the process of combining wholesale services
is difficult because the format of a wholesale service
API is different among first-B companies [4].
The core function that resolves these issues is the
API orchestrator in MOOSIA. For a second-B company that wishes to launch a new collaborative service, the API orchestrator facilitates the use of wholesale services instead of developing the entire service
from scratch. This approach can drastically speed upservice development by a second-B company. Specifically, it can accelerate the cycle shown at theupper left of Fig. 1 that consists of three phases: combining wholesale services and launching a new service (phase 1), operating the newly provided service
as desired (phase 2), and improving the provided service (phase 3). Since the closed loop described earlier
is called the 1st loop, we call this cycle the 2nd loop.
The API orchestrator defines the specifications of apublicly available wholesale service as a catalog. It
defines the specifications of a collaborative service
by creating a federated catalog that combines multiple catalogs of publicly available wholesale services(Fig. 2) [5]. In this catalog-driven manner, the API
orchestrator generically executes multiple services.
Here, we describe each function of the API orchestrator in Fig. 2. First, the REST (Representational
State Transfer) API sending/receiving section
receives TMF-compliant APIs (such as an order for acollaborative service). The catalog/resource management section, meanwhile, manages TMF-compliant
catalogs (refer, create, update, and delete). Next, thescenario execution/management section inputs therequested order and executes (create, update, delete,etc.) the associated wholesale services according to a
scenario based on the corresponding federated catalog. In addition, the API adapter section absorbs thedifferences between TMF APIs, to which the API
orchestrator conforms, and APIs provided by wholesale services. Then, that section executes the wholesale services.
This kind of architecture provides loose coupling
between the API adapter and scenario execution/
management section. It is beneficial because we canflexibly add new services or APIs by simply adding
new catalogs and API adapters. The lifecycle management section, moreover, manages the lifecycles ofassembled resources after building a service, while italso performs monitoring and fault detection. For
second-B companies, the above functions and features simplify the combination of multiple wholesale
services and the creation/management of collaborative services, thereby achieving phases 1–3 in the 2nd
NTT Technical Review

Feature Articles

Federatedcatalogs

Second B(service provider)

Combines multiple catalogs tocreate a federated catalog
Catalog 1
NW
operator 1

Cloud
A

Catalog 2
NW
operator 2

Cloud

API orchestrator

B

Catalogcall

Catalog/resource
management section

CatalogCatalog
NW camera

Catalog
NW
operator 1

Catalog
NW
operator 2

Handles publicly
available services as
catalogs

Catalog
Cloud
operator A

Catalog
Cloud
operator B

REST API sending/receiving
sectionScenario execution/
management
section

Lifecycle
management
sectionAPI adapter section
NW camera

NW
operator 1

NW
operator 2

Cloud
operator A

Cloud
operator B

API for each
operator
Cloud
operator A

NW operator 1
NW operator 2

Cloud
operator B

End
user

NW: network
REST: Representational State Transfer

Fig. 2. Overview of API orchestrator.

loop shown in Fig. 1.
5. Technologies for enhancing service assurance
and fulfillment in MOOSIA
We introduce here two technologies that respectively enhance phases 2 and 3 in the 2nd loop.
The technology for phase 2 measures the end-toend quality of service. Although the operation APIsof a first-B company’s wholesale services provide
monitoring, detection, troubleshooting, or other functions, they are often limited to guaranteeing the service level agreement (SLA) within only a wholesale
service. That means functions have not been sufficiently prepared for securing end-to-end quality in acollaborative service provided by a second-B company to end users. This technology therefore deploys
a quality-collection agent on the U-plane (userplane), such as on a cloud, to determine/control thequality experienced by an end user on an end-to-end
level.
The technology for phase 3 makes recommendations on service configuration. A wholesale service
includes many configuration parameters, and therefore, establishing the settings when initially building
the service is not a simple task. Consequently, having
second-B companies provide answers to some simple
Vol. 16 No. 6 June 2018questions makes it possible for this technology topresent potential network/cloud service configurations and parameter settings. The technology can also
predict performance based on configuration information and current operating conditions after the service
launch and can also present an optimal reconfiguration and resetting plan whenever performance is outof bounds. This approach helps to guarantee performance, minimize costs, and thereby improve the service overall.
6. API framework
We refer here again to the issue described in section
4 (issue 3), which is the difficulty of automating theprocess of combining wholesale services due to different formats in the wholesale service APIs among
first-B companies. One possible solution to this issue
is to provide common API specifications in order tosimplify a first-B company’s task of designing theAPI. Additionally, for a second-B company, we can
expect common operations regardless of the service
being used thanks to the standardized common API
specifications (as prescribed by TMF). For example,
the business process involved in ordering a service
does not depend on the service being provided, so we
consider that API specifications corresponding to that
4

Feature Articles

process can be standardized in a service-independent
manner.
To establish API specifications for an operation, we
have been establishing (1) a target business process,(2) a data model for the data to be used in the processof step (1), and (3) the functions for achieving theprocess in step (1) using the data of step (2) and ameans of deploying those functions in the operation
system. In actuality, though, API specifications canbe defined as a method of data distribution between
the functions established in (3) based on the data
model in (2) with respect to the business process in(1). We call a general system for establishing such
API specifications an API framework. In addition, weare studying an API framework focusing, for example, on specific API specifications such as an API forchecking the resource inventory of first-B companies
and on functions for bundling multiple APIs such as
to coordinate the authentication functions of a second-B company and a first-B company. In relation to
the above, TMF is working to establish operationreference models that include common APIs, so weplan to upstream the results of our studies [6].

trator is now reaching a practical level of implementation.
Going forward, we will continue to research anddevelop MOOSIA technologies to enable customers
to create new services as needed by combining individual services. We will also enable customers tooperate and expand such services with the aim ofmaking it easier to create, operate, and improve services.
References
[1]

[2]

[3]

[4]

7. Future evolution of MOOSIA
In fiscal year 2016, we teamed up with a partner
service provider to conduct trials that combined commercial service APIs provided by both an NTT Group
company and a non-NTT company. The results of thistrial showed that we successfully established catalogbased API orchestrator technology. This API orches-

5

[5]

[6]

S. Horiuchi, K. Akashi, M. Sato, and T. Kotani, “Network Resource
Management Technology,” NTT Technical Review, Vol. 15, No. 10,2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201710ra2.html
K. Watanabe, Y. Ikeda, Y. Nakano, K. Ishibashi, R. Kawahara, and S.
Suzuki, “Creating New Value by Leveraging Network-AI Technology
in Service Operations,” NTT Technical Review, Vol. 16, No. 6, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201806fa3.html
M. Kobayashi, R. Matsumura, T. Kimura, T. Tsuchiya, and K. Noritake, “A Study on Effective Resource Allocation Method to Virtual
Network with Service Accommodation Degree,” IEICE Technical
Report, Vol. 115, No. 404, pp. 29–34, 2016 (in Japanese).
Y. Soejima, M. Nakajima, and K. Takahashi, “One-stop Operation
Technology,” NTT Technical Review, Vol. 14, No. 10, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201610fa5.html
H. Tanaka, N. Tateishi, and A. Yoshida, “Development of Operations
for On-demand Design and Provision Based on Service Provider’sRequests,” Business Communication, Vol. 54, No. 5, pp. 10–11, 2017
(in Japanese).
A. Suzuki, K. Kashibuchi, and T. Nakamura, “Activities toward TM
Forum Frameworx 17.0 and TM Forum Live! 2017 Report,” NTT
Technical Review, Vol. 16, No. 1, 2018.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201801gls.html

NTT Technical Review

Feature Articles

Takaaki Moriya

Research Engineer, Operation Innovation Project, NTT Network Service Systems Laboratories.
He received a B.E., M.Sc., and Ph.D. in engineering from the University of Tokyo in 2001,
2003, and 2011. He joined NTT Network Service
Systems Laboratories in 2003, where he studied
IPv6 mobility, web-telco collaboration, and othertopics. He also worked on commercial service
development of Internet protocol interconnection
between international carriers (e.g., Long Term
Evolution (LTE) roaming). He is currently studying operation technologies including MOOSIA.
He received the Young Researcher’s Award fromthe Institute of Electronics, Information andCommunication Engineers (IEICE) in fiscal year
2009.

Atsushi Yoshida

Research Engineer, Operation Innovation Project, NTT Network Service Systems Laboratories.
He received a Bachelor of Science and Engineering from Ritsumeikan University, Kyoto, in
2003, and a Master of Information Science andTechnology from the University of Tokyo in
2005. He joined NTT Network Service Systems
Laboratories in 2005. He is currently studying
operation technologies including MOOSIA.

Masahiro Kobayashi

Research Engineer, Communication Traffic &Service Quality Project, NTT Network Technology Laboratories.
He received a B.S. and M.S. in information
science from Tohoku University, Miyagi, in 2007
and 2009. He joined NTT in 2009. He is currently studying optimal resource control in thevirtualized communication network at NTT Network Technology Laboratories. He is a memberof IEICE.

Shigeaki Harada

Senior Research Engineer, Communication
Traffic & Service Quality Project, NTT Network
Technology Laboratories.
He received a B.S., M.S., and Ph.D. in information science from Tohoku University, Miyagi, in
2001, 2003, and 2006. Since joining NTT Network Technology Laboratories (formerly NTT
Service Integration Laboratories) in 2006, he has
been researching traffic analysis and traffic control in IP networks. He is a member of IEICE.

Shingo Horiuchi

Yoshihiko Ito

Researcher, Operation Innovation Project,
NTT Network Service Systems Laboratories.
He received a B.E. and M.E. in informatics
from Kyoto University in 2009 and 2011. In
2011, he joined NTT Network Service Systems
Laboratories. He is currently studying operation
technologies including MOOSIA. He is a member of IEICE.

Vol. 16 No. 6 June 2018

Senior Research Engineer, Access Network
Operations Project, NTT Access Network Service Systems Laboratories.
He received a B.E. and M.E. in engineeringfrom the University of Tokyo in 1999 and 2001.
He joined NTT Access Network Service Systems
Laboratories in 2001, where he has been
researching and developing access network
operation systems. He has also been engaged instandardization work for operations support systems in TM Forum as a member of the Zerotouch Orchestration, Operations & Management(ZOOM) Project since 2014. He is a member of
IEICE.

6

Feature Articles: Creating a Flexible and Smart
Network as a New Social Infrastructure

Global Collaboration Initiatives
Revolutionizing Research andDevelopment of Network
Technologies
Takeshi Kuwahara, Hitoshi Irino, and Ken-Ichi Suzuki
Abstract

To transform the network into a new social infrastructure, it is essential that the research and development (R&D) scheme for network systems be revolutionized not only by promoting more tie-ups thanever before with diverse business partners but also by collaborating with global partners in various
phases of the R&D process. This article presents examples of these activities focusing on the APAC
(Asia-Pacific) Telecom Innovation Initiative (ATII) now in progress and collaborative efforts with carriers and other partners based outside of Japan.
Keywords: network R&D, open innovation, global expansion

1. Global collaboration in network research anddevelopment (R&D)
At the NTT laboratories, we have been working to
expand NTT technology globally through standardization activities and other efforts and have therefore
formed collaborative tie-ups with carriers and business partners such as vendors outside Japan. Now,
with the aim of transforming the network into a newsocial infrastructure, we plan to form collaborative
tie-ups with global partners at various R&D phases
driven through open innovation, and to promote more
tie-ups than ever before with diverse business partners. These efforts reflect the importance of forminga new ecosystem while revolutionizing the R&D
scheme and promoting accelerated R&D.
To be more specific, we are beginning to engage
with Asia-Pacific (APAC) carriers in addition to carrying out our traditional activities with European andAmerican carriers through standards developing
organizations (SDOs). Our approach here is to undertake joint studies from the early stages of R&D on use
cases, technology applicability, and common carrier
1

requirements. APAC has high growth potential as atelecommunications market compared with Europe
and the United States, and future investment in newtechnologies and services for the telecommunications
infrastructure can be expected. APAC also includes
many countries having diverse regional characteristics, and this is expected to lead to the development of
new services based on diverse needs in the telecommunications market and information and communication technology (ICT) industry. Collaborating witha variety of players in the APAC region should stimulate innovation in the use of ICT and contribute to thecreation and expansion of a productive and affluent
society in the APAC region. Our aim here is to use
this engagement with APAC as an opportunity totransform the entire global industry.
In this article, we introduce the APAC Telecom
Innovation Initiative (ATII) now in progress and collaborative efforts with carriers and other partners
outside Japan as specific examples of our new
approach to network R&D.

NTT Technical Review

Feature Articles

Contributions tostandardization
Flexible access network
virtualization
PoC onmultilateral testbedServer platform
virtualization

High-value-added
network services

PoC in Indonesia
PoC: proof of concept

Fig. 1. Overview of ATII.

2. ATII R&D initiative and global disseminationfrom the APAC region
In April 2017, NTT and PT Telkomunikasi Indonesia (Persero) Tbk (hereinafter, TELKOM), Indonesia’s leading telecommunications provider, established ATII as an R&D initiative targeting virtualinfrastructure technologies with the aim of improving
communication services and creating new network
services in the APAC region [1]. The Vietnam Posts
and Telecommunications Group (VNPT) joined ATII
in September 2017, and at present, the initiative participants are actively working to disseminate information from the APAC region on a global scale(Fig. 1). Joint studies are being conducted in the ATII
toward the creation of new network services with aview to 2020 and beyond. Moreover, with the aim ofsolving common problems in the APAC region, joint
technical studies and proofs of concept (PoCs) arebeing carried out on “Flexible and Smart Network”
technologies with a focus on virtual infrastructuretechnologies. Three work projects (WPs) have so far
been launched to conduct joint studies and PoCs.
(1) WP1: High Value-added Network Services
We conducted a joint PoC with TELKOM on smart
building services applying Data Stream Assist techVol. 16 No. 6 June 2018

nology [2] at a TELKOM building in Bandung, Indonesia (Fig. 2). From here on, we plan to conduct morestudies toward field trials of such services.
(2) WP2: Server Platform Virtualization
We conducted a joint PoC based on a virtual customer premises equipment (vCPE) use case. The
demonstration involved controlling a TELKOM virtual firewall (vFW) by using NTT virtual machine
manager technology [3] via an interconnection
between TELKOM (Bandung) and NTT (Tokyo –Kuala Lumpur) test beds (Fig. 3). With the knowledge gained here, we are improving elemental technologies and planning to popularize and expand thisscheme by contributing it to open source software(OSS) communities.
(3) WP3: Flexible Access Network Virtualization
We conducted a joint study on access network virtualization based on Flexible Access System Architecture (FASA) [4, 5] and undertook the drafting ofcommon specifications by extracting common
requirements from use cases brought to the study. We
released the results of this study as a white paper andare now promoting standardization activities withTELKOM and other European and American carriers
at the Broadband Forum (BBF).
At ATII, we plan to make proposals and suggestions
2

Feature Articles

Smart parking x Smart security

Smart access x Smart security

Parking

Smart
accessSmartsecurity

Smartparking

Fig. 2. Example of high-value-added network services.

TELKOM

NTT

Bandung, Indonesia
OpenStackcompute node

Kuala Lumpur,
Malaysia
OpenStackcontroller nodeTokyo, Japan

VM manager
IP-VPN

IP-VPN

Operation
devicevFW

Traffic
generator

IP-VPN: Internet protocol virtual private network
VM: virtual machine

Fig. 3. Joint PoC on multi-country test beds.

to SDOs and industry alliances by pursuing more
joint studies and PoCs as we go forward. We also seek
to expand this initiative by encouraging the participation of more service providers and partners.
3. Activities regarding access network
virtualization (FASA) with an eye toglobal expansion
FASA was proposed in February 2016 by the NTT
laboratories as a concept related to the development
3of future access network technology. We explain thelatest activities involving FASA in this section.
3.1 FASA activities to date
The aim with FASA is to enable end users to accessdiverse services in a quick and low-cost manner andto enable service providers (the second ‘B’ in the
B2B2X (business-to-business-to-X) model) to provide services promptly. To realize the FASA concept,
we released a FASA application programming interface (API) in phases as white papers in May 2016 and
NTT Technical Review

Feature Articles

R-CORD: PoC of use casefor optical access network
systems based on
ONOS

Applications

OpenFlow
controller
ONOS (Open Network
Operating System):development of open
network operating system

Cloud Central Office: standardizationof next-generation central office basedon virtualization technologies

Disaggregated upper
layer PON functions
are implemented on
network servers.

Configuration
controller

H/W abstraction layer (HAL)YANG model for PON(PON can be controlled as
one component.)
API for time-critical applications(NTT’s proposal)

vOLT-HA: development ofopen HAL

vOLT H/W
DBA

YANG model for ONU(Next-generation ONU
management channel interface)

Functional
block

Time-criticalapplicationsAPI

HAL for time-critical functions
ONF

ONU

ONU
External H/W

BBF
DBA: dynamic bandwidth assignment
HA: hardware abstraction
H/W: hardware

ONU: optical network unit
vOLT: virtual optical line terminal
YANG: Yet Another Next Generation

Fig. 4. F
 ASA R&D based on cooperation between organizations in standardization projects andthe use of open source software.

February 2017, promoting it as a commonly usable
API.
3.2 C
 ollaboration with other organizations onaccess network virtualization
The Central Office Re-architected as a Datacenter(CORD) project for virtualizing and reconfiguring atelecom carrier’s central office in the same way as adatacenter is being moved forward in the Open Networking Foundation (ONF). In particular, studies on
passive optical network (PON) virtualization arebeing conducted in the R-CORD (Residential CORD)
project targeting subscribers (access the network). In
this project, we are realizing FASA through verification experiments on use cases based on FASA
(Fig. 4).
The BBF, moreover, has launched the standardization of Cloud Central Office corresponding to CORD
in ONF, and in terms of access, it is standardizing
specifications for architecture targeting softwareVol. 16 No. 6 June 2018

defined networking (SDN) access nodes and for
YANG (Yet Another Next Generation) data modules
for use in configuring and managing access network
equipment. The abstraction layers of the virtual OLT(optical line terminal) are being investigated to communalize the equipment interface that is dependenton hardware.
There are many areas in which these standardization activities overlap with FASA, so we are cooperating with other organizations to reflect FASA
requirements and specifications in those areas andaccelerate research on FASA. Furthermore, as part of
the FASA activities, we have proposed a new standardization project at BBF on the virtualization oftime-critical applications such as DBA (dynamic
bandwidth assignment) and have commenced related
standardization activities [6].
Additionally, as part of our access virtualization
efforts in ATII WP3 described above, we are extracting common requirements among APAC carriers and4

Feature Articles

incorporating them into FASA to communalize
FASA technology. In short, we aim to globalize
FASA and related technologies by studying FASA
use cases using OSS, standardizing technologies thatreflect FASA requirements, and communalizing
FASA technologies among carriers.
4. Joint experiment with Chunghwa Telecomusing white-box switches
In this section, we report on a joint experiment conducted with Chunghwa Telecom of Taiwan in 2017.
4.1 C
 arrier collaboration with Chunghwa Telecom
Telecom carriers have not only sought to lower
costs in recent years but have also expressed a growing need to have networks that can respond quickly
and flexibly to dramatic increases in traffic anddiverse usage scenarios. To this end, it is becoming
increasingly important to use globally common andgeneral-purpose technologies and products instead of
conventional carrier-dedicated network products.
The NTT laboratories have begun collaboratingwith Chunghwa Telecom on the application of common and general-purpose technologies and products
to various areas of a carrier network (network edge,
core backbone network, and datacenter network).
Activities such as these are helping to form stronger
inter-APAC-carrier ties. In this collaboration, a memorandum of understanding on joint research and joint
experiments was signed in February 2017, and a joint
experiment was successfully conducted at Chunghwa
Telecom Laboratories in Taoyuan City, Taiwan, from
November 20 to December 1, 2017 [7].
4.2 V erification of virtual-network configurationand control technologies
The NTT laboratories have been developing MultiService Fabric (MSF) technology as part of their
R&D efforts in configuring a network with generalpurpose equipment [8]. MSF is technology for configuring a highly reliable network using simple general-purpose equipment while also being an SDN
architecture supporting multi-vendor network equipment. This technology was first released as OSS in
October 2017 [9]. In this architecture, the MSF controller interacts with upper-level systems using REST
(Representational State Transfer) and controls target
switches using a configuration and control protocol
such as NETCONF (Network Configuration Protocol). Additionally, with the assumed use of a Clos*
5

network topology consisting of switches incorporating a general-purpose data-transfer ASIC (application specific integrated circuit) such as white-box
switches, MSF can achieve virtual networks such as a
Virtual eXtensible Local Area Network (VXLAN) or
layer-3 virtual private network (VPN) using MPLS
(Multi-Protocol Label Switching).
Chunghwa Telecom, meanwhile, has proposed auniform control method for multiple SDN architectures using a GUI (graphical user interface) and has
developed an orchestrator called Network Adapter
with Programmability and Automation (NAPA) thatcan easily grasp network conditions. NTT and Chung­hwa Telecom conducted a joint experiment on linking
NAPA and MSF to verify virtual-network configuration and control technologies using an Ethernet VPN
VXLAN running on an operating system for whitebox switches (Fig. 5). In addition to verifying theinteroperability of both companies’ technologies, thisjoint experiment simulated faults that can occur incarrier network operation in order to verify a level ofreliability that ensures service continuity can be provided. For example, the experiment verified that
redundancy measures at the time of a fault in the MSF
controller and automatic switching of communication paths at the time of a fault in white-box switches
were functioning adequately.
NTT and Chunghwa Telecom plan to use theknowledge gained in this joint experiment to conduct
more detailed technical studies and tests toward theuse of general-purpose products such as white-box
switches in commercial services. Moreover, at NTT,we will disseminate these experimental results andthe knowledge gained from this collaboration in open
communities such as Telecom Infra Project and ONF
with the aim of gaining more advocates of this technology on a global basis.
5. Future development
The NTT laboratories will continue to collaborate
with diverse global partners and promote accelerated
R&D for network technologies driven by open innovation with the aim of achieving a network that creates new value for its users.

*

Clos: A network connection format in which all lower-tier network devices (leaves) are connected to all upper-tier network devices (spine) with no network devices on the same tier (fellow
leaf devices or fellow spine devices) connected to each other.

NTT Technical Review

Feature Articles

Orchestrator

Scope of this joint experiment
Chunghwa Telecom
Orchestrator (NAPA)

VIM

NTT
SDN
controller

MSF controller

APL
VM
White-box switch OS
White-box switch

Commodity
switch (black box)

Vendorswitch

APL
VM

APL
VM

ServerSupport for various commodity
products
Diverse SDN architecture
to meet user needs
APL: application
OS: operating system
VIM: virtualized infrastructure manager

Fig. 5. Joint experiment with Chunghwa Telecom using white-box switches.

References
[1]
[2]

[3]

[4]
[5]

Press release issued by NTT on April 17, 2017.
http://www.ntt.co.jp/news2017/1704e/170417a.html
N. Higo, R. Ishibashi, Y. Sueda, Y. Takita, Y. Hamaishi, and Y. Kaieda,“A Proposal for the Distributary Function to Assist Multi-purpose
Usages of Data-streams,” IEICE Technical Report, Vol. 117, No. 33,
pp. 5–10, 2017 (in Japanese).
M. Hamada, E. Iwasa, and K. Kuriu, “Virtual Machine Management
Technology for Operating Packet Switching System in a Virtualization Environment,” NTT Technical Review, Vol. 15, No. 12, 2017.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201712ra2.html
FASA, http://www.ansl.ntt.co.jp/e/global/FASA/index.html
M. Yoshino, H. Ujikawa, T. Harada, R. Yasunaga, T. Mochida, K.
Asaka, J. Kani, and K. Suzuki, “New Access System ArchitectureVol. 16 No. 6 June 2018

[6]
[7]
[8]

[9]

(FASA)—Enabling Provision of Services Flexibly and Promptly,”
NTT Technical Review, Vol. 14, No. 10, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201610fa3.html
Press release issued by the Broadband Forum on December 6, 2017.
https://www.broadband-forum.org/news/download/pressreleeases/2017/
PR13_BBF_NTTProject_FINAL.pdf
Press release issued by NTT on December 12, 2017.
http://www.ntt.co.jp/news2017/1712e/171212a.html
K. Takahashi, H. Yoshioka, K. Ono, and T. Iwai, “Promoting the MSF
Architecture for Flexible Networks,” NTT Technical Review, Vol. 14,
No. 10, 2016.
https://www.ntt-review.jp/archive/ntttechnical.php?contents=
ntr201610fa6.html
Multi-Service Fabric (MSF), https://github.com/multi-service-fabric/msf

6

Feature Articles

Takeshi Kuwahara

Senior Research Engineer, Supervisor, Group
Leader, Innovative Network Technology andCollaboration Group, NTT Network Technology
Laboratories.
He received a B.E. and M.E. from Waseda
University, Tokyo, in 1995 and 1997. Since joining NTT in 1997, he has been engaged in R&D
of ATM networks, IP-VPNs, cloud systems, network security, and edge computing technologies.
He was in charge of telecom carrier relations
regarding network R&D while based in Washington, D.C., USA, and led the establishment of
ATII (APAC Telecom Innovation Initiative)
while in his current position. He is a member of
the Institute of Electronics, Information andCommunication Engineers (IEICE).

Hitoshi Irino

Senior Research Engineer, Transport Network
Innovation Project, NTT Network Service Systems Laboratories.
He received a B.A. in environmental information and an M.A. in media and governance (I.T.)
from Keio University in 2003 and 2005. He
joined NTT Network Service Systems Laboratories in 2005. He researched traffic analysis technologies using flow information such as NetFlow/IPFIX (IP Flow Information Export) andproposed drafts about IPFIX in the Internet Engineering Task Force as part of standardization
efforts. He has been one of the original architectsof NTT’s SDN technology for Multi-Service
Fabric (MSF) since July 2014. He also initiated a
project to construct an original networking OS
called “Beluganos” using OSS and OpenFlow onmerchant silicon-based switches. He is a memberof IEICE.

7

Ken-Ichi Suzuki

Senior Research Engineer, Supervisor, NTT
Access Network Service Systems Laboratories.
He received a B.E. and M.E. in electronicengineering from Utsunomiya University, Tochigi, in 1988 and 1990, and a Ph.D. in information
science and technology from Hokkaido University in 2009. He joined NTT in 1990, where hehas been engaged in R&D of optical communications systems. He is currently working on the
FASA project and has also been involved in R&D
of optical access related systems/technologies
including next-generation optical access systems
and long-reach PON systems based on burstmode optical-amplifier and digital coherent
technologies. He has served as Vice Chair of theIEEE (Institute of Electrical and Electronics
Engineers) 1904 Access Networks Working
Group, Chair of the Optical Access Test Implementation Liaison Committee in HATS (Harmonization of Advanced Telecommunication Systems) Conference of Japan, and Chair of WP3,
the Project for Flexible Access System Virtualization, of ATII. Dr. Suzuki is a member of OSA(The Optical Society), IEEE, and IEICE.

NTT Technical Review

Regular Articles

Traffic Prediction and Quality
Evaluation for Improving
Communication Quality during
Major Events
Daisuke Ikegami and Jun Okamoto
Abstract

NTT Network Technology Laboratories is studying event-traffic prediction and quality evaluation forpreventing deterioration in communication quality due to a sudden increase in traffic during major
events. In this study, we model user movement and communication behavior and predict traffic volumes
and communication quality on the day of an event by prior simulation.
Keywords: traffic prediction, multi agent simulation, communication behavior

1. Countermeasures to traffic during
major events
Recent years have seen explosive growth in the useof mobile terminals such as smartphones and tablets
and the emergence of diverse media services. As aresult, the communication applications for mobile
users have become increasingly diverse, and dataintensive communications as in video viewing andimage sharing on social networking services havebecome an everyday activity. This means that major
events such as fireworks displays and festivals—that
is, events where more than ten times the usual numberof people can come together at a specific time withina specific space—can potentially generate large volumes of mobile traffic that existing facilities andresources cannot easily accommodate. Consequently,
the problem arises as to how to estimate the communication resources needed and implement appropriate
countermeasures such as adding more facilities andresources in order to carry such a large volume oftraffic and prevent deterioration in communication
quality.
In general, past traffic data and actions implemented by operators can be stored for events that are held1

regularly, and such knowledge can be used when necessary to mount countermeasures to heavy traffic.
With this approach, however, it must be kept in mind
that the timetable and venue can change even for thesame event, resulting in temporal and spatial changes
in traffic. In addition, first-time events have no pasttraffic data, which makes accurate estimation of traffic volumes difficult.
2. Event-traffic prediction
NTT Network Technology Laboratories has proposed a method for predicting event traffic in the areasurrounding the event venue by using simulation that
inputs behavior rules of mobile terminal users basedon two key assumptions: (a) the behavior of people at
an event can be consolidated into typical behavior
according to event content, and (b) typical user
behavior for even a new event is equivalent to that ofa similar past event (Fig. 1). With the proposed
method, parameters such as map data and number ofusers are input into the user behavior model (behavior
rules), and traffic volumes generated by simulation incertain temporal and spatial granularities are output.
Here, the user behavior model consists of (1) a
NTT Technical Review

Regular Articles

Event-traffic prediction technology
User behavior model

Current
locationContext
Current
locationTargetlocationInter-userinteraction

Map data, numberof users

Total traffic

Movement
behavior

Communication
behavior
Communication
conditions
according tocontext

Traffic

Input parameters

Moving, idle, …

Time (e.g. 1 day)Temporal/spatial
prediction of traffic

Fig. 1. Event-traffic prediction method.

movement behavior model describing user movement, and (2) a communication behavior model
describing communication behavior, as summarized
below:(1)	The movement behavior model consists ofspatial movement rules in an event venue
space. It determines a user’s destination androute of movement within the time period
simulated.
(2)	The communication behavior model consistsof traffic generation rules of a mobile terminal
carried by a user. It determines the presence/
absence of traffic and the traffic volume
depending on the time, place, and surroundingarea.
In this study, we consider that user communication
behavior has a strong dependence on user context,
and we model this feature accordingly. For example,
we expect a user to communicate infrequently while
in motion and frequently while waiting in line.

any measurement point based on radio parameterssuch as interference and terminal receive strength.
However, since many people can be expected toassemble at the same time during an event, resulting
in communication conditions that are vastly different
from normal times, estimating radio parameters foran event day beforehand has been difficult. This, inturn, has made it difficult to estimate communication
quality.
In this study, we are focusing on the fact that theresults of user behavior will affect radio parameters
on event day and therefore estimate communication
quality at event time accordingly. Specifically, basedon traffic prediction results and a communication
resource deployment plan envisioned for an event, weestimate communication quality on event day by
inputting venue congestion and traffic flowing
through each resource—which relate to radio receive
strength and interference—into a mathematicalmodel (Fig. 2).

3. Event-time communication-quality evaluation

4. Toward further improvements incommunication quality

To accurately estimate the communication resources needed at the time of an event, we need to estimate
the communication quality for the predicted flow of
event traffic and specify the locations and times corresponding to a drop in communication quality. Techniques for estimating the quality of mobile communications have been proposed in the past. These techniques are used to estimate communication quality at
Vol. 16 No. 6 June 2018

In this article, we introduced a study on methods ofevent traffic prediction and communication quality
evaluation for dealing with sudden jumps in traffic
associated with major events. These methods involve
conducting prior simulations to predict mobile-user
behavior that affects mobile traffic on event day andto estimate communication quality at event time.
2Regular Articles

Resource deployment plan

Event-traffic prediction
technology

Results of traffic prediction
Results of quality evaluation

Terminal user having simulated
event-day behavior

Fig. 2. Overview of quality evaluation technique based on event traffic.

These methods can be applied to facility design, as inderiving optimal locations for deploying communication resources. In the future, we expect that thesemethods will be applied in estimating and improving
communication quality during the international
sports event happening in Tokyo in 2020 and duringother major events held throughout Japan.
However, from the viewpoint of monitoring andcontrol, it is necessary to deal with changes in

Daisuke Ikegami

Associate Manager, NTT Network Technology
Laboratories.
He received a B.E., M.E., and Ph.D. fromWaseda University, Tokyo, in 2002, 2004, and2007. Since joining NTT Service Integration
Laboratories in 2007, he has been researching IP(Internet protocol) service network engineering.
He also carried out promotional activities related
to research and development technologies at
NTT Beijing Representative Office from 2012 to2015. He received the Young Engineer Award
from the Institute of Electronics, Information andCommunication Engineers (IEICE) in 2010. He
is a member of IEICE.

3

mobile-user behavior due to scheduling delays on
event day and the holding of special events. Given thepossibility of real-time and detailed network control
by 5G (fifth-generation mobile communications systems) in the future, we plan to study the applicationof this technology to real-time control based on
observed traffic and communication quality data in
addition to prior facility design as described in thisarticle.

Jun Okamoto

Senior Research Engineer, Supervisor, Service
Assessment Group, Communication Traffic &Service Quality Project, NTT Network Technology Laboratories.
He received a B.S. and M.S. in electrical engineering from the Tokyo University of Science in1994 and 1996. He joined NTT in 1996 and has
been engaged in the quality assessment of visualcommunication services. He is currently working
on developing subjective and objective methods
to assess audio/visual communication services.
He has been contributing to VQEG (Video Quality Experts Group) since 2004. He received theTelecommunications Advancement Foundation
Award in 2009 and the International Telecommunication Union Encouragement Award inJapan in 2010.

NTT Technical Review

Regular Articles

Investigating Countermeasuresagainst High-altitude Electromagnetic
Pulse (HEMP)
Masato Maruyama, Ken Okamoto, Yuichiro Okugawa,and Jun Kato
Abstract

The threat of attack by a nuclear explosion triggering a high-altitude electromagnetic pulse (HEMP)
has become an actuality. HEMP has the capacity to destroy a wide range of electric and electronicequipment. We report here on initiatives being studied to reduce the damage caused by a breakdown ofcommunications and to restore communications promptly.
Keywords: HEMP, pulse, nuclear

1. Introduction
The recent deterioration in the international situation means that an attack by high-altitude electromagnetic pulse (HEMP) is becoming a more realistic
threat. HEMP is a very powerful electromagnetic
wave generated when a nuclear explosion occurs at
an altitude of 30–400 km (Fig. 1). Heat rays, blasts,
and radiation caused by a nuclear explosion would
not reach the ground because of the very high detonation altitude. However, strong electromagnetic waves
would be radiated over a wide area (radius of 1000 km)
at ground level.
HEMP is capable of destroying a wide variety ofelectric and electronic equipment. As a result, all systems including electricity, gas, water and sewage,transportation, broadcasting, and communication
would be deprived of control functions and possibly
break down and stop functioning over a wide geographical area [1]. In particular, disruption or severing of communications would be a major barrier torestoration and further exacerbate the initial damage.
Therefore, the most important task is to devise countermeasures to HEMP in order to protect communication equipment.
One effective countermeasure would be to protect
1

key facilities by installing electromagnetic-wave
shielding and noise filters in equipment. However,from a cost perspective, it is impossible to protect
every piece of equipment. Therefore, in the first
instance, it is essential to clarify the possible flow-on
effects of disruption to communication equipment
caused by HEMP and to consider appropriate (necessary and sufficient) protective measures. Specifically,
we first have to know the maximum intensity of
HEMP that equipment might be exposed to (therequired proof stress for the equipment), and theintensity of electromagnetic waves that the equipment can withstand without failing (the actual proof
stress of the equipment). Then, if the actual proof
stress of the device is below what is required, necessary measures to compensate for the shortfall
revealed by the proof stress test will have to be taken.
2. Countermeasures against HEMP to protect
telecom equipment
Here, we explain the issue of required proof stressand actual proof stress of equipment.
2.1 Required proof stress of equipment
HEMP has been discussed at various academic
NTT Technical Review

Regular Articles

Ionosphere

E2 HEMP

E3 HEMPEarth
E3 HEMP
10 mV/m Max.
From 1 s–500 s after explosion(Resembles solar storm)

E1 HEMP50 kV/m Max.
From immediately after explosion to 1 ms after(Resembles electrostatic discharge)

E2 HEMP100 V/m Max.
From 1 ms–1 s after explosion(Resembles lightning surge)

Fig. 1. Depiction of HEMP attack.

conferences and standardization meetings since the1990s, and in 2009, Recommendation ITU-T K.78
“High altitude electromagnetic pulse immunity guidefor telecommunication centres” was approved by theInternational Telecommunication Union - Telecommunication Standardization Sector (ITU-T) Study
Group 5 [2]. This standard describes the proof stressrequirement for telecom and power equipment foreach way (seven ways in total) in which HEMP can
affect them. However, the proof stress required forthe equipment depends on the environment in which
the equipment is installed (such as the building structure and the presence or absence of countermeasuresagainst lightning strikes). Therefore, we are evaluating the equipment installation environment based on
the NTT Standard Operation Procedure and past electromagnetic wave measurement results.
2.2 Actual proof stress of equipment
To measure the proof stress of equipment, it is necessary to expose the equipment to an electromagnetic
pulse imitating HEMP. However, the electromagnetic

Vol. 16 No. 6 June 2018

pulse created by HEMP is extremely strong and has avery steep rise characteristic, so determining how to
generate the pulse and expose the equipment to it is achallenging task.
3. Future plans
We are currently evaluating the communication
device installation environment, examining the electromagnetic pulse application test method, and building a test environment in cooperation with the NTT
EAST Technical Assistance and Support Center.
Going forward, we plan to collaborate with the Disaster Prevention Planning Office of each of the NTT
operating companies and carry out proof stress tests
on various kinds of equipment.
References[1][2]

T. Onizuka, “Threat of High Altitude Electromagnetic Pulse (HEMP)
Attack,” CISTEC Journal, No. 166, pp. 130–140, 2016 (in Japanese).
Recommendation ITU-T K.78: “High altitude electromagnetic pulse
immunity guide for telecommunication centres,” 2009.

2

Regular Articles

Masato Maruyama

Senior Research Engineer, EMC Technology
Group, Environmental Technology and Management Project, NTT Network Technology Laboratories.
He received a B.E. and M.E. in electronics
engineering from the University of ElectroCommunications, Tokyo, in 2001 and 2003. He
joined NTT Energy and Environment Systems
Laboratories in 2003 and engaged in studies on
stabilization of direct current power supply systems and optimization of air conditioning fortelecom equipment rooms. He has been withNTT Network Technology Laboratories since2015. His current research interest is electromagnetic compatibility for telecommunication systems.

Ken Okamoto

Senior Research Engineer, EMC Technology
Group, Environmental Technology and Management Project, NTT Network Technology Laboratories.
He received a B.E. and M.E. in science from
Nagoya University in 2004 and 2006. He joined
NTT Energy and Environment Systems Laboratories in 2006 and engaged in studies on spatiotemporal image analysis in video monitoring ofslope failure to detect a sign of the disaster. He iscurrently studying the electromagnetic compatibility (EMC) technology for telecommunications.

3

Yuichiro Okugawa

Senior Research Engineer, EMC Technology
Group, Environmental Technology and Management Project, NTT Network Technology Laboratories.
He received a B.E. and M.E. in electrical engineering from Tokyo University of Science in2002 and 2004. He joined NTT Energy and Environment Systems Laboratories in 2004 andstudied the EMC technology for telecommunications. He has been engaged in quantitative evaluation using electromagnetic simulation about the
influence of electromagnetic field on communication equipment by HEMP. He is a member of
the Institute of Electronics, Information andCommunication Engineers (IEICE).

Jun Kato

Senior Research Engineer, Supervisor, EMC
Technology Group, Environmental Technology
and Management Project, NTT Network Technology Laboratories.
He received a B.E. from Shizuoka University
in 1992. He joined NTT Telecommunication
Networks Laboratory in 1992, and has been
engaged in research and development of EMC
for telecommunication systems. He is currently asupervisor of EMC Group at NTT Network Technology Laboratories.

NTT Technical Review

Global Standardization Activities

Automotive Edge Computing
Consortium—a Global Effort to
Develop a Connected Car Platform
Koya Mori
Abstract

NTT launched the Automotive Edge Computing Consortium with various global partners, namely,
DENSO Corporation, Ericsson, Intel Corporation, NTT DOCOMO, Toyota InfoTechnology Center Co.,
Ltd., and Toyota Motor Corporation, in order to develop technologies for connected cars using automotive big data. The objective of the consortium is to form an ecosystem for connected cars to support
emerging services such as intelligent driving, the creation of maps with real-time data, and driving assistance based on cloud computing.
Keywords: edge computing, connected car, automotive big data

1. Introduction

2. Issues in developing connected car platform

Safer driving, minimized traffic delay, effective
resource consumption, and a lower level of air pollution are some of the demands of the future society
where a profound increase in mobile telecommunication usage for connected cars is expected. In advanced
automotive services such as intelligent driving, data
generation for real-time maps, and cloud-assisted
driving, it is expected that a huge number of datatransfers between connected cars and the cloud will
be inevitable. By 2025, it is predicted that the amountof data exchanged between connected cars and thecloud will reach 10 exabytes per month [1]. This is10,000 times the amount of data exchanged today.
Thus, technologies such as distributed networking
and system architectures with computing resourcesthat can accommodate the processing of a hugeamount of data are needed.
To enable the fulfillment of these demands and topromote the widespread use of the new technology,
NTT, alongside DENSO Corporation, Ericsson, Intel
Corporation, NTT DOCOMO, Toyota InfoTechnology Center, and Toyota Motor Corporation, established the Automotive Edge Computing Consortium(AECC) [2].

A capacity issue arises with the current mobile network and cloud computing system in accommodatinga large number of connected cars effectively. In a
network topology expressed in a tree form as thebasic architecture of a mobile network, traffic on sucha network converges at the narrow top end, and thiscauses a huge concentration of data from/to connected cars. Moreover, data concentration is an evenmore serious issue because the current cloud computing is also located at converged datacenters. These
heavy concentrations of data cause a slow responsetime and long processing time between connectedcars and the cloud system and are an obstacle toimplement a platform to serve a large number of connected cars.
Therefore, the purpose of the AECC is to design asystem architecture for a connected car platform toconvey and process a huge amount of data with anappropriate network and assignment of computing
resources (Fig. 1).

1

3. Concept of AECC
One solution to the aforementioned issue is to
NTT Technical Review

Global Standardization Activities

Data

Data

Data

Cloud

Highresolution
mapIntelligent
driving

V2C2V

Data processing
too concentrated

Too much data
traffic on network

Network

ECU/Sensor

Connected car

Video stream

Still image

Connected car

Map

Connected car

Connected car

10–100 million connected cars
ECU: electronic control unit
V2C2V: vehicle-to-cloud-to-vehicle

Fig. 1. Issues of current mobile network architecture [1].

Distributed computing:
Reduces concentration ofcomputation to shorten
processing time

Cloud

Local
data

Local
data

Data

Data

Local
data

Localized network:
Splits data traffic in orderto handle huge data flow
Local
data

Highresolution
mapIntelligent
driving

V2C2V

Data

Highresolution
mapIntelligent
driving

V2C2V

Distributed computing

Distributed computing

Localized network

Connected car

Local
data

Local
data

Localized network

Edge computing

Connected car

Connected car

Connected car

Fig. 2. Utilization of edge computing [1].

distribute computing resources on a localized network. When computing and network resources are
distributed locally, each local system can handle asmall amount of data traffic and implement timely
responses to connected cars.
The distributed network and computation featuresof edge computing make it a promising technologythat could be adopted to realize this concept. The
network is designed to split data traffic into several
localities that cover reasonable numbers of connected
Vol. 16 No. 6 June 2018cars. The computation resources are hierarchically
distributed and layered in a topology-aware fashionto accommodate localized data and to allow large
volumes of data to be processed in a timely manner(Fig. 2).
Edge computing technology will thus provide an
end-to-end system architecture framework used to
distribute computation processes to localized networks. Therefore, the AECC will focus on increasing
capacity to accommodate automotive big data in a2

Global Standardization Activities

The future use cases of connected car
Research anddevelopment

Autonomous
driving

Automotive
datacentertion• CAN data
• ECU data
• Cruise control• Driver’s condition

Network

reaEdge serverHighresolution
map

eca

se

Edge computing platform

Us

Traffic

Intelligent
driving

V2C2V

liz
a

• Intelligent driving• High-resolution map• Cruise assist

Distributed high-speed
big data processing by
utilizing edge computing

Cloud

Access network• Traffic control• Traffic info.
• Mobility sharing• Multimodal

Challenge*:
Processing enormous
dataVideo/Still ImageECU/Sensor

Map

CameraConnected car

Connected car

Connected car* By 2025, the data volume each month will reach 10 exabytes, which is 10 times the current data volume.
CAN: controller area network

Fig. 3. Concept of AECC.

reasonable fashion between vehicles and the cloud bymeans of edge computing technology and more efficient network designs. The consortium will define
requirements and develop use cases for emerging
mobile services, with a particular focus on the automotive industry (Fig. 3).
4. Global spread of technology
There are important steps needed to realize AECC’svision, starting with developing and implementing
technologies surrounding connected cars on a global
scale, fostering the global acceptance of a common
system architecture, and involving related industries
in the construction of the architecture. To achieve thismission, we brought together partners from different
fields of industry including automotive, telecommunications, and information technology to establish the
AECC. With a strong global partnership, we want to3

push our requirements as an input to standards developing organizations and industry bodies to achieve
global standards. In concrete form, we want toendorse the aforementioned edge computing, networks, and architecture to accommodate huge volumes of traffic and the creation of corresponding
analysis tools for data processing to realize intelligent
driving, data generation for real-time maps, andcloud-assisted driving. For that reason, we will continue our efforts in defining the requirements for thesystem architecture and creating the necessary platform that will enable future automotive services.
References
[1]

[2]

Automotive Edge Computing Consortium (AECC) White Paper,“General Principle and Vision,” Version 1.0.0, 2017.
https://aecc.org/wp-content/uploads/2017/12/AECC_Whitepaper_
v1.0.0.pdf
Website of AECC, https://aecc.org/

NTT Technical Review

Global Standardization Activities

Koya Mori

Senior Research Engineer, NTT Network Innovation Laboratories.
After joining NTT in 2004, he researched anddeveloped an Internet of Things application
based on software technologies such as the OSGi
(Open Services Gateway Initiative), OpenStack,and edge computing. He is a member of theInstitute of Electronics, Information and Communication Engineers (IEICE).

Vol. 16 No. 6 June 20184

Practical Field Information about
Telecommunication Technologies
Practical Field Information about Telecommunication Technologies

Investigation of Unintentional
One-ring Outgoing Calls
Abstract

This article describes an actual problem case involving unintentional one-ring outgoing calls. The
Network Interface Engineering Group investigated the mechanism of the problem and devised somesolutions to prevent a recurrence. This is the forty-sixth article in a series on telecommunication technologies. This contribution is from the Network Interface Engineering Group, Technical Assistance andSupport Center, Maintenance and Service Operations Department, Network Business Headquarters,
NTT EAST.
Keywords: PBX, IP phone, IP packet capture

1. Introduction

2. Problem overview and investigation method

fore contacted the maintenance provider of their private branch exchange (PBX). By checking the calllogs of the PBX, the maintenance provider found thatthe one-ring calls were coming from one particularnumber. Therefore, User A also contacted the callersuspected of making the one-ring phone calls so as tosolve the problem. The caller (User B) was using theoptical IP telephone service called HIKARI DENWA
OFFICE A (ACE) with the Netcommunity SYSTEM
αNX II type-L (NXIIL) phone system. The NXIIL
system data indicated that the telephone number ofUser A was registered in the phonebook of the NXIIL
system. We checked the call history data saved on allof the phones deployed in User B’s system and determined that only one particular phone was used to call
User A. However, the user of the phone claimed that
he had not make the phone calls. Suspecting a device
failure, the field maintenance personnel performed
the usual maintenance procedures such as turning thepower of the NXIIL main equipment off/on andreplacing the central control unit (CCU)*1. These
procedures did not solve the problem, however, so theTechnical Assistance and Support Center conducted
an investigation of the matter.
The configuration of the user networks and datacollection points is shown in Fig. 1. We collected: (1)A customer (User A) recognized that the one-ringcalls were happening several times a day and there-*1 CCU: A circuit board unit that performs IP packet routing andcall processing of the system.

The Network Interface Engineering Group conducts investigations and provides technical support tosolve highly technical problems that occur in Internet
protocol (IP) network services such as FLET’S
HIKARI NEXT. In the investigations, we often capture and analyze IP packets to find out the root causesof the problem and then present practical solutions toenable continuous use of the services by our customers. However, IP network services accommodate not
only an Ethernet-based device but also a VoIP (voiceover Internet protocol) gateway, which converts IP
signals to non-IP signals such as analog or integrated
services digital network (ISDN) signals. Therefore, itis necessary to choose proper measurement devices
such as oscilloscopes, protocol analyzers, or otherterminal-specific measurement devices to collect andanalyze data when we face technical problems.
In this article, we introduce a case involving unintentional one-ring calls that occurred in a businessphone system using extension phones. The details of
the problem and our analysis method are also presented in the following sections.

1

NTT Technical Review

Practical Field Information about Telecommunication Technologies

24IP standard
phones × 75 units
NXIIL

PBX
Next
Generation
Network

FLET’S HIKARI
NEXT

ONU

HIKARI DENWA
OFFICE A 32 channels,
20 numbers
User A(call-terminating side)

ONU: optical network unit

Power supply
hub × 20 units

Switchinghub (S724)

User B(call-originatingside)
Gigabit-compatible
LAN distribution
protocol checker
monitoring system

24IP cordlessphones × 4 units

Fig. 1. Connection configuration and data collection points.

incoming call logs of the PBX at the call-terminatingside ( ), (2) IP packets on the Ethernet section with apacket capture device ( ), (3) IP extension phone
operation commands with an αLAN distribution
monitoring system*2 ( ), and (4) system data on the
NXIIL system ( ). By collecting and analyzing suchdata, we can determine where the problem originated.
3. Analysis of collected data
We describe in this section how we analyzed thedata to detect the root of the problem.
3.1 Incoming call log of PBXThe incoming call log on the User A side PBX
showed that extremely short calls (less than one second from receipt to disconnection of call) occurred
four times during the investigation. Moreover, all of
the calls had the same identification number registered in the NXIIL system.
3.2 IP packet capture data
We analyzed the captured IP packet data and foundthat INVITE signals (request signals to establish callsin the session initiation protocol (SIP)*3) were sentfrom User B to User A. Note that the sending time of
the INVITE signals all agreed with the recorded timeof the one-ring calls in the PBX logs described above.
Furthermore, CANCEL request signals (shutdown
signals of the ongoing session in SIP) were also sentfrom the User B side. Therefore, the calls were terminated by the reception of 487 Request Terminated(termination of request signals in SIP) messages sentfrom the User A side (Fig. 2).
Vol. 16 No. 6 June 2018

3.3 αLAN distribution monitoring system log
The actual user operations of the IP extensionphone on the User B side were revealed by analyzing
the data collected by the αLAN distribution monitoring system. The estimated user operations were as
follows; first, the phonebook button was pushed
twice. Then an off-hook/on-hook operation was done.
Note that these were done within approximately one
second (see Table 1).
3.4 NXIIL main equipment system data
Upon checking the common phonebook data, wefound that the telephone number of User A was registered as a first entry when the user selects a furigana(Japanese syllabary character) search function.
4. Mechanism of unintentional one-ring calls
We found that User B used the off-hook externalline capture function in the NXIIL system. This
enables the user to make an outgoing call only by an
off-hook operation—without pushing any number
buttons—when an arbitrary number registered in thephonebook function is selected (i.e., when the number is displayed on the telephone’s liquid crystal display (LCD) panel).
*2 αLAN distribution monitoring system: A local area network distribution monitoring system compatible with α series businessphones. (Other monitoring systems are for bus distribution or star
distribution.)*3 SIP: A control protocol used in HIKARI DENWA and many IP
phones to perform various procedures pertaining to calls, including calling of the other party, notifying the caller that the otherparty is being called, and managing route information of thecalled party.

2

Practical Field Information about Telecommunication Technologies

NXIIL (User B)

PBX (User A)

SIP server

INVITE(Connection request)
100 Trying(Notification of processing)

INVITE(Connection request)
100 Trying(Notification of processing)180 Ringing

180 Ringing(Calling)(Calling)
CANCEL(Shutdown of ongoing session)
200 OKCANCEL(Shutdown of ongoing session)
200 OK487 Request Terminated(Termination of request)
ACK
487 Request Terminated(Acknowledgment)(Termination of request)
ACK(Acknowledgment)

Fig. 2. SIP sequence.

Table 1. IP extension outgoing call operations (excerpt).
Time

Status display

Explanation

17:40:12

Phonebook

Phonebook button pressed
Phonebook button pressed

17:40:13

Phonebook

17:40:13

Off-hook

Receiver picked up

17:40:14

LK7 private-use

Line key 7 captured

17:40:14

On-hook

Receiver put down

When a business phone is connected to a HIKARIDENWA line or an ISDN line, the call session isestablished in the first few milliseconds. Therefore,
the outgoing signal is delivered to the other party
3even when an on-hook operation is performed rightafter the off-hook. As a result, the call appears as a
one-ring call on the User A side (Table 2). Note thatthe one-ring call does not occur when a business
NTT Technical Review

Practical Field Information about Telecommunication Technologies

Table 2. Mechanism for occurrence of unintentional one-ring calls.
Operation sequence

Telephone operation

1

2

Speed
dial
Call

Speed
dial
Call

Furigana searchfunction in phonebook
selected

Relevant phonebook
registration namePhonebook registration
name of the relevant
number selected

Phonebook3

Off-hook

4On-hook

5. Inference of cause
From the captured data described in the previous
section, we speculated the sequence of events as follows. First, the user (User B) pushed the phonebook
button in order to use the “a-ka-sa-ta-na” (A-B-C-DE) line search function of the phonebook. However,the user accidentally pressed the phonebook button
twice. Then the user tried to clear the display by
doing off-hook/on-hook operations since the first
telephone number entry in the phonebook (wrong
destination number) was displayed on the LCD. As aresult, the call appeared as a one-ring call on the User
A side. In general, pushing a clear button on the telephone is recommended to cancel the operation
instead of using the off-hook/on-hook operation.
6. Countermeasures
We proposed the following three countermeasures
to prevent unintentional calls to a particular telephone
number.
(1)	Users should be sure to use the clear button
rather than performing off-hook/on-hook
operations to clear the telephone number
shown on the LCD.

StatusFurigana search

Phonebookphone is connected to an analog line.

Vol. 16 No. 6 June 2018

LCD panel

Relevant telephone
numberTime/Day/Day of the week/
Month
Extension number/
Extension name

Available external line
captured and outgoingcall to the relevant
number madeLine disconnected(2)	Register a dummy telephone number (e.g., an
unused, short-digit number) as the first entry
in the phonebook to avoid making an unintentional call, If the user prefers to use off-hook/on-hook operations instead of the clear button
to cancel the operation, no call will go throughto the dummy number.
(3)	Disable the off-hook external line capture
function of the business phone to prevent
automatic external line capture when performing off-hook operations.
The customer of the call-originating side (User B)
decided to perform countermeasures (1) and (2). As aresult, no unintentional one-ring outgoing calls havesubsequently occurred.
7. Conclusion
In this article, we introduced a problem case involving unintentional one-ring calls in a business phonesystem using extension phones. The problem wassolved by analyzing captured Ethernet IP packets andcontrolling signals between the main business phoneequipment (NXIIL) and the IP extension phone. The
Technical Assistance and Support Center works diligently to solve such troublesome problems andensure continuous service for our customers.

4

Short Reports

Successful Joint Test of Nextgeneration Onboard IoT Platform

1. Introduction
The NYK Group (Nippon Yusen Kabushiki Kaisha
and MTI Co., Ltd.) and the NTT Group (NTT and
NTT DATA) have successfully conducted a proof-ofconcept experiment for a next-generation onboard
Internet of Things (IoT) platform. The test was conducted aboard Hidaka, a domestic coastal vessel
owned and operated by Kinkai Yusen Kaisha Ltd. of
the NYK Group.
2. Background and details of experiment
The NYK Group previously developed a ship information management system (SIMS) that enables thegathering, monitoring, and sharing of detailed data

Onboardsensors

ECDIS

VDR
Operational data

Engine datalogger (D/L)
Engine data

Edge
serverOperates
applications
for utilizing
onboard data
ShipDCapp.

(A)
Primary
analysis

between ship and shore, with the aim of promoting
safe and efficient operations. The data include information on the operational condition and performanceof oceangoing vessels.
In September 2017, the four companies began collaborating to make use of NTT’s edge computing
technology in order to develop a next-generation
onboard IoT platform by adding to SIMS a new system that would enable the remote distribution andmanagement of onboard applications from land
offices [1]. This experiment was conducted using
NTT DATA’s ANYSENSE IoT platform and the communication company’s expertise in developing infrastructure that makes use of IoT solutions (Fig. 1).
This platform is also compliant with onboard IoTinternational standardization being devised by the

Shore/
ShipDC
officeApplication distribution
management server

Application
distribution(B)
ViewerIoT data
sharing platform

Cloud forship data

Dualog system

Compliant with
ISO/FDIS19847,
ISO/FDIS19848 [2]Java/OSGi
framework

Java/OSGi
application
distribution

Ship’s LAN

IoT data collection/application execution platform
NYK/MTI(B) Viewerapp.

(A) Primary
analysis app.

NTT Group

ECDIS: electronic chart display and information system
ISO/FDIS: final draft International Standard of the International
Standards Organization.

NTT DATA’s IoT platform
Experiment target

LAN: local area network
OSGi: Open Services Gateway Initiative
VDR: voyage data recorderFig. 1. Next-generation onboard IoT platform.

1

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Short Reports

Japan Ship Machinery and Equipment Association
and under development by the International Standards Organization (ISO) [2]. In addition, the gathered data can be utilized not only on board but also
from an IoT open platform ShipDC, a ship datacenterestablished by Nippon Kaiji Kyokai (Class NK).
3. Future prospects
The four companies performing this test will next
conduct a proof-of-concept experiment on an oceangoing vessel operated by the NYK Group. They will
also continue to strive for innovation in the maritime
industry in order to improve the safety and efficiency
of vessel operations, and will promote environmental
initiatives and global competitiveness.

Vol. 16 No. 6 June 2018

References
[1]

[2]

Joint press release issued by the NYK Group, the NTT Group, andDualog AS on September 19, 2017, “NYK Group and NorwegianPartner Dualog Begin Collaboration with NTT Group for On-board
IoT Innovation.”
http://www.ntt.co.jp/news2017/1709e/170919a.html
ISO/FDIS19847: Ships and marine technology -- Shipboard dataservers to share field data at sea, and ISO/FDIS19848: Ships andmarine technology -- Standard data for shipboard machinery andequipment.

For Inquiries
Research and Development Planning Department,
NTT
http://www.ntt.co.jp/news2018/1802e/180215a.html

2External Awards
Encouraging Award
Winner: Daisuke Hisano, NTT Access Network Service Systems
Laboratories
Date: July 27, 2017
Organization: The Institute of Electronics, Information and Communication Engineers (IEICE) Technical Committee on Communication Systems
For “Evaluation of TDM-PON System Aggregating TDD-based
Mobile Fronthaul and Secondary Services.”
Published as: D. Hisano, T. Shimada, H. Ou, T. Kobayashi, Y.
Nakayama, J. Terada, and A. Otaka, “Evaluation of TDM-PON System Aggregating TDD-based Mobile Fronthaul and Secondary Services,” IEICE Tech. Rep., Vol. 116, No. 401, CS2016-66, pp. 13–18,
2017.
Encouraging Award
Winner: Takahiro Suzuki, NTT Access Network Service Systems
Laboratories
Date: July 27, 2017
Organization: IEICE Technical Committee on Communication Systems
For “Parallel Implementation of Cipher on CPU/GPU for Programmable Optical Access Equipment.”
Published as: T. Suzuki, S. Kim, J. Kani, K. Suzuki, and A. Otaka,“Parallel Implementation of Cipher on CPU/GPU for Programmable
Optical Access Equipment,” IEICE Tech. Rep., Vol. 116, No. 9,
CS2016-1, pp. 1–6, 2016.
2018 OSA Fellow Member
Winner: Takashi Saida, NTT Device Innovation Center
Date: January 1, 2018
Organization: The Optical Society (OSA)
For his seminal contribution to advances in waveguide-type polarization devices for coherent optical networks.
Female Researcher Encouragement Award
Winner: Yoko Ono, NTT Device Technology Laboratories
Date: March 10, 2018
Organization: The Electrochemical Society of JapanFor her research and development of photocatalytic and battery
electrode materials for energy-generation and energy-storage technologies.
Young Scientist Presentation Award
Winner: Tetsuhiko Teshima, NTT Basic Research Laboratories
Date: March 17, 2018
Organization: The Japan Society of Applied Physics (JSAP)
For “Reconstruction of Micro-scale Tissues in Self-folded Microrolls.”
Published as: T. Teshima, H. Nakashima, Y. Ueno, S. Sasaki, C.
Henderson, and S. Tsukada, “Reconstruction of Micro-scale Tissues
in Self-folded Micro-rolls,” The 78th JSAP Autumn Meeting, Fukuoka, Japan, Sept. 2017.

1

OFT Young Researcher’s Award
Winner: Shingo Ohno, NTT Access Network Service Systems Laboratories
Date: March 21, 2018
Organization: IEICE Technical Committee on Optical Fiber Technologies (OFT)For “Distributed Spatial Mode Dispersion Measurement along
Strongly Coupled Multicore Fiber Using Rayleigh Backscattering
Interference Pattern.”
Published as: S. Ohno, K. Toge, D. Iida, and T. Manabe, “Distributed Spatial Mode Dispersion Measurement along Strongly Coupled
Multicore Fiber Using Rayleigh Backscattering Interference Pattern,” IEICE Tech. Rep., Vol. 117, No. 323, OFT2017-48, pp. 23–28,
2017.
Young Researcher’s Award
Winner: Atsushi Nakamura, NTT Access Network Service Systems
Laboratories
Date: March 22, 2018
Organization: IEICE
For his research on applications of highly sensitive microbending
detection using a 1-µm-band mode-detection optical time domainreflectometer (OTDR) and on a long-range 1-µm-band mode-detection OTDR and its field test.
Young Researcher’s Award
Winner: Kenji Miyamoto, NTT Access Network Service Systems
Laboratories
Date: March 22, 2018
Organization: IEICE
For “Experimental Evaluation of Mobile Fronthaul Optical Bandwidth and Wireless Transmission Performance for Base Station
Architecture Splitting LTE PHY Layer Functions in Downlink” (inJapanese) and “Proposal of Low Latency Symbol Level Transmission
Scheme in Intra PHY Split Base Station” (in Japanese).
Published as: K. Miyamoto, T. Shimizu, J. Terada, and A. Otaka,“Experimental Evaluation of Mobile Fronthaul Optical Bandwidthand Wireless Transmission Performance for Base Station Architecture Splitting LTE PHY Layer Functions in Downlink, ” Proc. of the
IEICE General Conference 2017, B-8-44, Nagoya, Japan, Mar. 2017.
K. Miyamoto, T. Shimizu, J. Terada, and A. Otaka, “Proposal of Low
Latency Symbol Level Transmission Scheme in Intra PHY Split Base
Station,” Proc. of the IEICE Society Conference 2017, B-5-42,
Tokyo, Japan, Sept. 2017.
Young Researcher’s Award
Winner: Saki Nozoe, NTT Access Network Service Systems Laboratories
Date: March 22, 2018
Organization: IEICE
For “Low Crosstalk 125 μm-cladding 2LP-mode 4-core Fiber with
Air-hole Structure.”
Published as: S. Nozoe, T. Sakamoto, T. Matsui, Y. Amma, K. Take­naga, Y. Abe, K. Tsujikawa, S. Aozasa, K. Aikawa, and K. Nakajima, “Low Crosstalk 125 μm-cladding 2LP-mode 4-core Fiber with
Air-hole Structure,” IEICE Tech. Rep., Vol. 117, No. 323, OFT2017-54,

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Papers Published in Technical Journals and Conference Proceedings

pp. 67–70, 2017.

Japan, Mar. 2017.

Young Researcher’s Award
Winner: Junki Ichikawa, NTT Network Innovation Laboratories
Date: March 22, 2018
Organization: IEICE

SASIMI Young Researcher Award
Winner: Saki Hatta, NTT Device Innovation Center
Date: March 26, 2018
Organization: The Institute of Electrical and Electronics Engineers(IEEE) Council on Electronic Design Automation

For “A Context Generation Method for Utilization of IoT-resources.”
Published as: J. Ichikawa, Y. Minami, J. Yamamoto, and K. Shimano, “A Context Generation Method for Utilization of IoT-resources,”
Proc. of the IEICE General Conference 2017, B-18-28, Nagoya,Japan, Mar. 2017.
Young Researcher’s Award
Winner: Hirofumi Sasaki, NTT Network Innovation Laboratories
Date: March 22, 2018
Organization: IEICE
For “An Analysis of Gaussian Beam Propagation for Orbital Angular Momentum (OAM) Multiplexing” and “An Experimental Evaluation of Gaussian Beam Propagation with Multiple Uniform Circular
Array in 28 GHz Band.”Published as: H. Sasaki, D. Lee, H. Fukumoto, T. Nakagawa, and H.
Shiba, “An Analysis of Gaussian Beam Propagation for Orbital
Angular Momentum (OAM) Multiplexing,” Proc. of the IEICE General Conference 2017, B-17-4, Nagoya, Japan, Mar. 2017.
H. Sasaki, D. Lee, H. Fukumoto, and H. Shiba, “An Experimental
Evaluation of Gaussian Beam Propagation with Multiple Uniform
Circular Array in 28 GHz Band,” Proc. of the IEICE Society Conference 2017, B-17-29, Tokyo, Japan, Sept. 2017.

For “Area-efficient Programmable Finite-state Machine Toward
Next Generation Access Network SoC.”
Published as: S. Hatta, N. Tanaka, and T. Sakamoto, “Area-efficient
Programmable Finite-state Machine Toward Next Generation AccessNetwork SoC,” Proc. of the 21st Workshop on Synthesis and System
Integration of Mixed Information Technologies (SASIMI 2018),
Matsue, Japan, Mar. 2018.
Maejima Hisoka Award
Winner: Ken Nakamura and Takayuki Onishi, NTT Media Intelligence Laboratories; Koyo Nitta, NTT Device Innovation Center
Date: April 10, 2018
Organization: Tsushinbunka Association
For their development of H.265 video encoding compatible largescale integrated circuit called NARA and the encoder incorporating
NARA, as well as contribution to practical implementation of H.265
coding.

Young Researcher’s Award
Winner: Takuro Fujii, NTT Device Technology Laboratories
Date: March 22, 2018
Organization: IEICEIPSJ Best Paper Award
Winner: Akihiro Miyata, Nihon University; Takashi Isezaki,
Masanao Nakano, Tatsuya Ishihara, Reiko Aruga, NTT Service Evolution Laboratories; Takayoshi Mochizuki, NTT Research and Development Planning Department; Tomoki Watanabe, NTT Service
Evolution Laboratories; Osamu Mizuno, NTT Intellectual Property
CenterDate: June 6, 2018
Organization: Information Processing Society of Japan (IPSJ)

For “1.3-μm InGaAlAs-based Directly Modulated Membrane
Distributed Reflector Laser Array” (in Japanese).
Published as: T. Fujii, H. Nishi, K. Takeda, E. Kanno, K. Hasebe, T.
Kakitsuka, T. Tsuchizawa, and S. Matsuo, “1.3-μm InGaAlAs-based
Directly Modulated Membrane Distributed Reflector Laser Array,”
Proc. of the IEICE General Conference 2017, C-3-21, Nagoya,

For “A Wheelchair Motion Estimation Model Using the Recent
Control Ability.”
Published as: A. Miyata, T. Isezaki, M. Nakano, T. Ishihara, R.
Aruga, T. Mochizuki, T. Watanabe, and O. Mizuno, “A Wheelchair
Motion Estimation Model Using the Recent Control Ability,” IPSJ
Journal, Vol. 57, No. 10, pp. 2316–2326, 2016.

Papers Published in Technical Journals andConference Proceedings
Network Resource Management Architecture with Unified
Information Models
S. Horiuchi, K. Akashi, M. Sato, and T. KotaniVol. 16 No. 6 June 2018

Proc. of the 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), Seoul, Korea, Sept. 2017.
With a view to the coming era of network virtualization, NTT is2

Papers Published in Technical Journals and Conference Proceedings

working on network operations that can flexibly manage diverse
types of networks including those currently in operation. This article
introduces network resource management technology for comprehensively operating a wide range of networks by expressing management specifications based on a uniform information model.

Power of Uninitialized Qubits in Shallow Quantum Circuits
Y. Takahashi and S. Tani
Proc. of the 35th Symposium on Theoretical Aspects of Computer
Science (STACS 2018), Article no. 57, Caen, France, Feb./Mar. 2018.
We study the computational power of shallow quantum circuitswith O(log n) initialized and nO(1) uninitialized ancillary qubits,
where n is the input length, and the initial state of the uninitialized
ancillary qubits is arbitrary. First, we show that such a circuit cancompute any symmetric function on n bits that is classically computable in polynomial time. Then, we regard such a circuit as an oracle
and show that a polynomial-time classical algorithm with the oracle
can estimate the elements of any unitary matrix corresponding to a
constant-depth quantum circuit on n qubits. Since it seems unlikelythat these tasks can be done with only O(log n) initialized ancillary
qubits, our results give evidence that adding uninitialized ancillary
qubits increases the computational power of shallow quantum circuits with only O(log n) initialized ancillary qubits. Lastly, to understand the limitations of uninitialized ancillary qubits, we focus on
near-logarithmic-depth quantum circuits with them and show the
impossibility of computing the parity function on n bits.

Batteryless Bicycle Speed Recorder with Hub Dynamo
and FM/AM Radio as Time Stamp Function
A. Tanaka, T. Nishihashi, H. Sakamoto, R. Suematsu, T. Kawanishi, and T. Douseki
IEEJ Transactions on Sensors and Micromachines, Vol. 138, No. 3,
pp. 79–86, March 2018.
A batteryless bicycle speed recorder with a hub dynamo and FM/
AM radio has been developed. The hub dynamo functions as both apower source and a speed sensor. It produces a voltage waveformwith fourteen AC cycles per rotation of the bicycle wheel, whichenables precise determination of speed and acceleration. Sound dataof radio broadcasting was also used as a time stamp function. The
data is stored in a magnetoresistive random-access memory(MRAM). A road test showed that a fabricated speed recorder
mounted on a bicycle stored and reproduced accurate values of alarge acceleration and its duration when the brakes were suddenly
applied while the bicycle was being ridden.

Integer Nesting/Splitting for Golomb-Rice Coding of Generalized Gaussian Sources
R. Sugiura, Y. Kamamoto, and T. Moriya
Proc. of 2018 Data Compression Conference, p. 427, Snowbird,
UT, USA, March 2018.
This paper presents a qualitative approach of combining GolombRice (GR) code with algebraic bijective mappings which losslessly
convert between arbitrary positive integers of different dimension
and shape the distribution of generalized Gaussian sources. The mappings and integer nesting and splitting enable GR encoding, with alittle additional computation, to compress more efficiently sources
based on wider classes of distributions than Laplacian. Simulations
showed, especially for some Gaussian sources, that almost optimal

3

average code length is achievable by performing integer nesting
before GR encoding the integers. This scheme will be useful for
applications dealing with various types of sources and requiring low
computational costs.

Piecewise Linear Predictive Coding for Nonlinear Signal
Analysis and Automatic Trend Extraction
F. Ishiyama
The IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, Vol. J101-A, No. 4, pp. 36–45,
April 2018 (in Japanese).
Linear predictive coding (LPC) is a widely used method of timeseries analysis, and Yule-Walker equation is used for the analysis.
However, as Yule-Walker equation replaces given time series with aperiodic time series, it is not suitable for transient analysis. Therefore,
we introduced a method of LPC without the replacement. In addition,
we consider a short enough time width, apply our LPC within thewidth, and obtain instantaneous characteristics such as frequencies
and decay rates within the width. We can find nonlinear characteristics of a given time series by applying the above analysis piecewisely and by comparing the neighboring pieces.

Access System Virtualization for Sustainable and Agile
Development
A. Otaka
IEICE Transactions on Communications, Vol. E101-B, No. 4, pp.
961–965, April 2018.
This paper describes why we require access system virtualization.
The purpose of access system virtualization is different from that ofcore network virtualization. Therefore, a specific approach should beconsidered such as the separation of software and hardware, interface
standardization, or deep softwarization.

Optimal Golomb-Rice Code Extension for Lossless Coding of Low-entropy Exponentially-distributed Sources
R. Sugiura, Y. Kamamoto, N. Harada, and T. Moriya
IEEE Transactions on Information Theory, Vol. 64, No. 4, pp.
3153–3161, April 2018.
This paper presents an extension of Golomb-Rice (GR) code forcoding low-entropy sources, in which the gap between their entropy
and the conventional GR code length gets larger. We mention here thefollowing four facts related to the proposed code, extended-domain
GR (XDGR) code: it is represented by multiple code trees based on
the idea of almost instantaneous fixed-to-variable length codes, with
its algorithm being a generalization of unary coding; its structure
naturally contains run-length coding; the gap between the entropy
and its average code length is theoretically guaranteed to be asymptotically negligible as the entropy of the exponentially distributed
sources tends to zero; and its coding parameter, corresponding to thenegative-domain Rice parameter of GR code, can be estimated fromthe input source-symbol sequence. Experimental evaluations are alsopresented supporting the theorems. The proposed XDGR code, having a simple algorithm and high compression performance, is
expected to be used for many coding applications that deal withexponentially distributed sources at low bit rates.

NTT Technical Review