This paper provides an overview of different experimental SDN/NFV control and orchestration architectures aiming at integrating the multi-layer (packet/optical) transport networks and distributed DC infrastructures (core cloud, edge computing) of the ADRENALINE testbed. The proposed SDN/NFV solutions enable the development and testing of end-to-end 5G and IoT services supporting a wide variety of use cases from different vertical industries, such as automotive, e-health, energy, media or smart cities. To this end, the ADRENALINE testbed is adopting the following technologies to meet the stringent requirements of 5G and IoT; i) high-capacity, flexible and cost/energy-efficient software-defined optical transmission technologies for access, metro and core networks; ii) highly-scalable packet technologies for aggregation and switching of flows with quality of service (QoS); iii) core datacenters and edge computing for the deployment of virtualized network functions (VNFs) and cloud services. The fifth generation of mobile technology (5G) and the Internet of Things (IoT) require the global orchestration of multi-layer (packet/optical) transport network and distributed (edge/core) datacenter (DC) infrastructure in order to provide the required end-to-end services (e.g. service function chaining) for multi-tenancy [1] . On the 5G side, the wide adoption of Network Function Virtualization (NFV) requires virtual computing and storage resources deployed throughout the network. NFV aims at deploying network functions that are typically deployed in specialized and dedicated hardware (e.g. mobile Evolved Packet Core -EPC, Base Band Unit -BBU), as software instances (named virtual network functions -VNF) running on commodity servers (e.g. in DCs) based on Commercial Off The Shelf (COTS) hardware. Traditionally, virtual computing and storage resources have been deployed in large DCs in the core network. Core DCs offer high-computational capacity with moderate response time, meeting the requirements of centralized services with low-delay demands. However, it is also required to offer edge computing (i.e., micro-DCs and small-DCs) in different sites of the mobile network (e.g., base stations, cells aggregation, radio network controller, central offices) leveraging on ultra/low-latency and high-bandwidth. For example, ETSI is defining the mobile edge computing (MEC) to offer applications such as video analytics, location services, mission-critical applications, augmented reality, optimized local content distribution and data caching. Additionally, the trend towards the Centralized Radio Access Networks (C-RAN) architecture, where the signal processing of the BBU is decoupled from the Radio Remote Head (RRH) located in the base station (BS), and moved to the central office, enables the virtualization of BBU functions in local DCs. On the IoT site, it is envisioned that billions of heterogeneous devices will be connected to the transport network using widely deployed wireless access technologies (e.g., Bluetooth, ZigBee, Wi-Fi, LoRa), fixed access technologies (e.g., PLC) or mobile technologies (e.g. GPRS/2G or eMTC and NB-IoT that will pave the way to 5G). Much like 5G, IoT also requires core computing and storage infrastructures in order to perform IoT analytics from the data collected of sensors and actuators (e.g., temperature monitoring, energy consumption measurement, etc.). However, collecting information and bringing it into one core-DC is not a long-term scalable solution, particularly as the volume of IoT devices and data is forecasted to explode. A solution recently proposed that addresses scale and efficiency is to distribute the processing of the IoT analytics from the core DC to the edge of the network (i.e., edge computing), known as edge IoT analytics. Preliminary analytics can be carried out on the edge cloud and only the necessary data or results are sent for further analysis (e.g. Big data) /storage in the core DC (e.g., simple analytics on the edge computing and the big data analytics on the core cloud). Additionally, some mission-critical IoT applications with very stringent delay requirements may also require to perform IoT analytics in the edge in order to perform real-time actions (e.g., a local valve can be turned off when a leak is detected). To address the challenge of jointly managing and operating heterogeneous transport network infrastructure and distributed cloud infrastructure for end-to-end 5G and IoT services, the adoption of reference architectures such as OpenStack, ONF/IETF Software Defined Networking (SDN), and ETSI NFV Management and Orchestration (MANO) is key to provide the global service orchestration. This paper first provides an overview of the transport and cloud infrastructures of ADRENALINE testbed [2] and then presents different experimental SDN/NFV control and orchestration architectures identifying the limitations of the available open source software implementations.
More details about the orchestration of network and cloud resources, identifying the limitations of the existing implementations is presented in REF .
Integrating optical transport network testbeds and cloud platforms to enable end-to-end 5G and IoT services
2017 19th International Conference on Transparent Optical Networks (ICTON)
2017 19th International Conference on Transparent Optical Networks (ICTON)
Engineering