Fog Computing and Its Role in the Internet of Things
Flavio Bonomi, Rodolfo Milito, Jiang Zhu, Sateesh Addepalli
Cisco Systems Inc.
170 W Tasman Dr. San Jose, CA 95134, USA{flavio, romilito, jiangzhu, sateeshk}@cisco.com

ABSTRACT

nodes in the vicinity to meet their delay requirements. An
emerging wave of Internet deployments, most notably theInternet of Things (IoTs), requires mobility support andgeo-distribution in addition to location awareness and low
latency. We argue that a new platform is needed to meetthese requirements; a platform we call Fog Computing [1],or, briefly, Fog, simply because the fog is a cloud close tothe ground. We also claim that rather than cannibalizing
Cloud Computing, Fog Computing enables a new breed ofapplications and services, and that there is a fruitful interplay between the Cloud and the Fog, particularly when it
comes to data management and analytics.
This paper is organized as follows. In the second section we introduce the Fog Computing paradigm, delineate its
characteristics, and those of the platform that supports Fogservices. The following section takes a close look at a few
key applications and services of interest that substantiate
our argument in favor of the Fog as the natural componentof the platform required for the support for the Internet ofThings. In the fourth section we examine analytics and big
data in the context of applications of interest. The recognition that some of these applications demand real-time analytics as well as long-term global data mining illustrates theinterplay and complementary roles of Fog and Cloud. We
conclude with comments about the state of the Fog Computing and discussion of future work.

Fog Computing extends the Cloud Computing paradigm tothe edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fogare: a) Low latency and location awareness; b) Wide-spread
geographical distribution; c) Mobility; d) Very large numberof nodes, e) Predominant role of wireless access, f) Strong
presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid , Smart
Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).

Categories and Subject Descriptors
C.2 [Computer-Communication Networks]: C.2.4 Computer Network Distributed Systems

Keywords
Fog Computing, Cloud Computing, IoT, WSAN, Software
Defined Networks, Real Time Systems, Analytics

1.

INTRODUCTION

The “pay-as-you-go” Cloud Computing model is an efficient alternative to owning and managing private data centers (DCs) for customers facing Web applications and batchprocessing. Several factors contribute to the economy ofscale of mega DCs: higher predictability of massive aggregation, which allows higher utilization without degradingperformance; convenient location that takes advantage ofinexpensive power; and lower OPEX achieved through thedeployment of homogeneous compute, storage, and networking components.
Cloud computing frees the enterprise and the end userfrom the specification of many details. This bliss becomesa problem for latency-sensitive applications, which require2.

THE FOG COMPUTING PLATFORM

2.1

Characterization of Fog Computing

Fog Computing is a highly virtualized platform that provides compute, storage, and networking services between
end devices and traditional Cloud Computing Data Centers,
typically, but not exclusively located at the edge of network.
Figure 1 presents the idealized information and computing
architecture supporting the future IoT applications, and illustrates the role of Fog Computing.
Compute, storage, and networking resources are the building blocks of both the Cloud and the Fog . “Edge of theNetwork”, however, implies a number of characteristics thatmake the Fog a non-trivial extension of the Cloud. Let us
list them with pointers to motivating examples.

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• Edge location, location awareness, and low latency.
The origins of the Fog can be traced to early proposals to support endpoints with rich services at theedge of the network, including applications with low
latency requirements (e.g. gaming, video streaming,augmented reality).

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the source. We elaborate in section 4 on the interplay
between Fog and Cloud regarding Big Data.

2.2

Fog Players: Providers and Users

It is not easy to determine at this early stage how thedifferent Fog Computing players will align. Based on thenature of the major services and applications, however, we
anticipate that:• Subscriber models will play a major role in the Fog (Infotainment in Connected Vehicle, Smart Grid, Smart
Cities, Health Care, etc.)• The Fog will give rise to new forms of competitionand cooperation between providers angling to provide
global services. New incumbents will enter the arena
as users and providers, including utilities, car manufacturers, public administrations and transportation
agencies.
Figure 1: The Internet of Things and Fog Computing

3.

• Geographical distribution. In sharp contrast to themore centralized Cloud, the services and applications
targeted by the Fog demand widely distributed deployments. The Fog, for instance, will play an activerole in delivering high quality streaming to moving vehicles, through proxies and access points positioned
along highways and tracks.

In this section we demonstrate the role the Fog plays inthree scenarios of interest: Connected Vehicle, Smart Grid,and Wireless Sensor and Actuator Networks.

3.1

Connected Vehicle (CV)The Connected Vehicle deployment displays a rich scenario of connectivity and interactions: cars to cars, cars toaccess points (Wi-Fi, 3G, LTE, roadside units [RSUs], smart
traffic lights), and access points to access points. The Fog
has a number of attributes that make it the ideal platform todeliver a rich menu of SCV services in infotainment, safety,traffic support, and analytics: geo-distribution (throughout
cities and along roads), mobility and location awareness,
low latency, heterogeneity, and support for real-time interactions.
A smart traffic light system illustrates the latter. The
smart traffic light node interacts locally with a number ofsensors, which detect the presence of pedestrians and bikers,
and measures the distance and speed of approaching vehicles. It also interacts with neighboring lights to coordinate
the green traffic wave. Based on this information the smart
light sends warning signals to approaching vehicles, and evenmodifies its own cycle to prevent accidents.
Re-coordinating with neighboring STLs through the orchestration layer of the Fog follows any modification of thecycle. The data collected by the STLs is processed to do
real-time analytics (changing, for instance, the timing of thecycles in response to the traffic conditions). The data fromclusters of smart traffic lights is sent to the Cloud for global,
long-term analytics.

• Large-scale sensor networks to monitor the environment, and the Smart Grid are other examples of inherently distributed systems, requiring distributed computing and storage resources.
• Very large number of nodes, as a consequence of thewide geo-distribution, as evidenced in sensor networks
in general, and the Smart Grid in particular.
• Support for mobility. It is essential for many Fog applications to communicate directly with mobile devices,and therefore support mobility techniques, such as theLISP protocol 1 , that decouple host identity from location identity, and require a distributed directory system.
• Real-time interactions. Important Fog applications involve real-time interactions rather than batch processing.
• Predominance of wireless access.
• Heterogeneity. Fog nodes come in different form factors, and will be deployed in a wide variety of environments.

3.2• Interoperability and federation. Seamless support ofcertain services (streaming is a good example) requires
the cooperation of different providers. Hence, Fog components must be able to interoperate, and services
must be federated across domains.

Smart Grid

Smart Grid is another rich Fog use case. We defer section 4 a discussion of the interplay of Fog and Cloud in thecontext of Smart Grid.

3.3• Support for on-line analytic and interplay with theCloud. The Fog is positioned to play a significant rolein the ingestion and processing of the data close to1

FOG COMPUTING AND THE INTERNET
OF THINGS

Wireless Sensors and Actuators Networks

The original Wireless Sensor Nodes (WSNs), nicknamed
motes [2], were designed to operate at extremely low power
to extend battery life or even to make energy harvestingfeasible. Most of these WSNs involve a large number of low

http://www.lispmob.org

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bandwidth, low energy, low processing power, small memory
motes, operating as sources of a sink (collector), in a unidirectional fashion. Sensing the environment, simple processing, and forwarding data to the static sink are the dutiesof this class of sensor networks, for which the open source
TinyOS2 is the de-facto standard operating system. Motes
have proven useful in a variety of scenarios to collect environmental data (humidity, temperature, amount of rainfall,light intensity, etc).
Energy constrained WSNs advanced in several directions:multiple sinks, mobile sinks, multiple mobile sinks, and mobile sensors were proposed in successive incarnations to meetthe requirements of new applications. Yet, they fall short inapplications that go beyond sensing and tracking, but require actuators to exert physical actions (open, close, move,focus, target, even carry and deploy sensors). Actuators,
which can control either a system or the measurement process itself, bring new dimensions to sensor networks.
The information flow is not unidirectional (from the sensors to the sink), but bi-directional (sensors to sink, andcontroller node to actuators). In a subtler, but significant
way, it becomes a closed-loop system, in which the issuesof stability and potential oscillatory behavior cannot be ignored. Latency and jitter become a dominant concern insystems that require rapid response.
S.S. Kashi and M. Sharifi [4] survey the contributions in
the coordination of Wireless Sensor and Actuator Networks(WSANs). They point out that in one architectural choice,
the WSAN consists of two networks: a wireless sensor network and a mobile ad hoc network (MANET). T. Bankaet al [6] stress that emergent applications demand a higher
bandwidth, collaborative sensing environment. Their experience is rooted in the CASA (Collaborative Adaptive Sensing of the Atmosphere) project. CASA [5], a multi-year,multi-partner initiative led by UMASS, deployed a networkof small weather radars, integrated with a distributed processing and storage infrastructure in a closed-loop system tomonitor the lower troposphere for atmospheric hazards liketornados, hailstorms, etc. Zink et al [3] provide technicaldetails of the deployment.
The characteristics of the Fog (proximity and location
awareness, geo-distribution, hierarchical organization) makeit the suitable platform to support both energy-constrained
WSNs and WSANs.

4.

third tier deal with visualization and reporting (human-tomachine [HMI] interactions), as well as systems and processes (M2M). The time scales of these interactions, all partof the Fog, range from seconds to minutes (real-time analytics), and even days (transactional analytics). As a resultof this the Fog must support several types of storage, fromephemeral at the lowest tier to semi-permanent at the highest tier. We also note that the higher the tier, the wider thegeographical coverage, and the longer the time scale. Theultimate, global coverage is provided by the Cloud, which
is used as repository for data that that has a permanenceof months and years, and which is the bases for businessintelligence analytics. This is the typical HMI environmentof reports and dashboards the display key performance indicators.

5.

CONCLUSIONS

We have outlined the vision and defined key characteristics of Fog Computing, a platform to deliver a rich portfolioof new services and applications at the edge of the network.
The motivating examples peppered throughout the discussion range from conceptual visions to existing point solutionprototypes. We envision the Fog to be a unifying platform,rich enough to deliver this new breed of emerging services
and enable the development of new applications.
We welcome collaborations on the substantial body ofwork ahead: 1) Architecture of this massive infrastructure ofcompute, storage, and networking devices; 2) Orchestration
and resource management of the Fog nodes; 3) Innovative
services and applications to be supported by the Fog.

6.

ACKNOWLEDGMENTS

We recognize the active participation of a number of collaborators, including Hao Hu, Preethi Natarajan, Xiaoqing
Zhu, Mythili Suryanarayana Prabhu, Mario Nemirovsky, Fulvio Risso.

7.

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[6] P. Tavel. Modeling and simulation design. 2007.

ANALYTICS, AND THE INTERPLAY BETWEEN THE FOG AND THE CLOUD

While Fog nodes provide localization, therefore enabling
low latency and context awareness, the Cloud provides global
centralization. Many applications require both Fog localization, and Cloud globalization, particularly for analytics andBig Data. We touched upon this point earlier in reference
to smart traffic light. Here we consider Smart Grid, whichdata hierarchies help illustrate further this interplay.
Fog collectors at the edge ingest the data generated bygrid sensors and devices. Some of this data relates to protection and control loops that require real-time processing(from milliseconds to sub seconds). This first tier of theFog, designed for machine-to-machine (M2M) interaction,collects, process the data, and issues control commands tothe actuators. It also filters the data to be consumed locally, and sends the rest to the higher tiers. The second and15