Review of implementing fog computing

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 04 Issue: 06 | June-2015, Available @ http://www.ijret.org 335
REVIEW OF IMPLEMENTING FOG COMPUTING
Pranali More1
1
MCA 3rd
Year, Mumbai University, 10/303 SamtaNagar Opp. Raymond company, Thane (w)-400606
Abstract
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog
provides data, compute, storage, and application services to end-users. It is a model in which data, processing and applications
are concentrated in devices at the network edge rather than existing almost entirely in the cloud. This document describes the
various features of Fog Computing and a case study along with the actual implementation of fog computing in traffic analysis to
understand how fog computing is applied to the edge environment. This document also contains the difference between the fog
computing and cloud computing.
Keywords— Fog Computing, Characteristics of Fog computing, Application of Fog computing, Difference between
Cloud computing and Fog Computing.
---------------------------------------------------------------------***--------------------------------------------------------------------
1 .WHAT IS FOG COMPUTING?
Fog Computing enables a new breed of applications and
services, and that there is a fruitful interplay between the
Cloud and the Fog, particularly when it comes to data
management and analytics. Fog Computing extends the
Cloud Computing paradigm to the edge of the network.
While Fog and Cloud use the same resources (networking,
compute, and storage), and share many of the same
mechanisms and attributes (virtualization, multi-tenancy)
the extension is a non-trivial one in that there exist some
fundamental differences that stem from the Fog raison
d'être. The Fog vision was conceived to address applications
and services that do not fit well the paradigm of the Cloud.
They include:
• Applications that require very low and predictable
latency—the Cloud frees the user from many
implementation details, including the precise knowledge of
where the computation or storage takes place. This freedom
from choice, welcome in many circumstances becomes a
liability when latency is at premium (gaming, video
conferencing).
• Geo-distributed applications (pipeline monitoring, sensor
networks to monitor the environment).
• Fast mobile applications (smart connected vehicle,
connected rail).
• Large-scale distributed control systems (smart grid,
connected rail, smart traffic light systems).
2. KEY CHARACTERISTICS OF THE FOG
COMPUTING
 Heterogeneity. 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.
Compute, storage, and networking resources are the
building blocks of both the Cloud and the Fog . ―Edge
of the Network‖, however, implies a number of
characteristics that make the Fog a non-trivial
extension of the Cloud. Let us list them with pointers
to motivating examples.
 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 the edge of
the network, including applications with low latency
requirements (e.g. gaming, video streaming,
augmented reality).
 Geographical distribution. In sharp contrast to the
more centralized Cloud, the services and applications
targeted by the Fog demand widely distributed
deployments. The Fog, for instance, will play an active
role in delivering high quality streaming to moving
vehicles, through proxies and access points positioned
along highways and tracks.
 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 the
wide 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 the LISP protocol, 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.
 Interoperability and federation. Seamless support of
certain services (streaming is a good example) requires
the cooperation of dierent providers. Hence, Fog
components must be able to interoperate, and services
must be federated across domains.

_______________________________________________________________________________________
3. FOG COMPUTING CASE SCENARIO
A Smart Traffic Light System (TLS):
TLS calls for the deployment of a STL at each intersection.
The STL is equipped with sensors that :
(a) Measure the distance and speed of approaching vehicles
from every direction;
(b) Detect the presence of pedestrians and cyclists crossing
the street.
The STL also issues ―slow down‖ warnings to vehicles at
risk to crossing in red, and even modifies its own cycle to
prevent collisions.
The TLS has three major goals:
(a) Accidents prevention;
(b) Maintenance of a steady flow of traffic (green waves
along main roads);
(c) Collection of relevant data to evaluate and improve the
system.
Note that the global nature of (b) and (c),in contrast with the
localized objective (a). Also note the wide difference in
timescales: (a) requires real time (RT) reaction, (b) near-real
time, and (c) relates to the collection and analysis of global
data over long periods.
• To be specific, consider some numbers. Let us say that the
green wave is set at 64 km/h (40miles/h). A vehicle moving
at 64 km/h travels 1.7m in 100 ms. Thepolicy requires
sending an urgent alarm to approaching vehicles when
collision with crossing pedestrians is anticipated. To be
effective the local control loop subsystem must react within
a few milliseconds—thus illustrating the role of the Fog in
supporting low latency applications. Accident prevention
trumps any other consideration. Hence, to prevent a
collision the local subsystem may also modify its own
cycle.1 In doing so it introduces a perturbation in the green
wave that affects the whole system. To dampen the effect of
the perturbation a re-synchronization signal must be sent
along all the traffic lights. This process takes place in a
timescale of hundreds of milliseconds to a few seconds.
Key Requirements Driven by the TLS:
This section discusses the requirements of a smart traffic
light system; the requirements are also highlighted in (Table
1)
1. Local subsystem latency: In TLS a subsystem includes
the traffic light, sensors
and actuators in a local region such that the reaction time is
on the order of<10 ms.
2. Middleware orchestration platform: The middleware
handles a number of critical software components across the
whole system, which is deployed across a wide geographical
area. The components include:
• The decision maker (DM), which creates the control
policies and pushes them to the individual traffic lights. The
DM can be implemented in a centralized, distributed or in a
hierarchical way. In the latter, the most likely
implementation, nodes with DM functionality of regional
scope must coordinate their policies across the whole
system. Whatever the implementation, the system should
behave as if orchestrated by a single, all knowledgeable
DM.
• The federated message bus, which passes data from the
traffic lights to the DM nodes, pushes policies from the DM
nodes to the traffic lights, and exchanges information
between the traffic lights.
3. Networking infrastructure: The Fog nodes belong to a
family of modular compute and storage devices. However
different the form factors, and the encasings (a ruggedized
version is required for the traffic light), they offer common
interfaces, and programming environment.
4. Interplay with the Cloud: In addition to the actionable
real-time (RT) information generated by the sensors, and the
near-RT data passed to the DM and exchanged among the
set of traffic lights, there are volumes of valuable data
collected by the system. This data must be ingested in a data
center (DC)/Cloud for deep analytics that extends over time
(days, months, even years) and over the covered territory.
In a cursory review of the potential value of this data we list:
(a) evaluation of the impact on traffic (and its consequences
for the economy and the environment) of different policies;
(b) monitoring of city pollutants; (c) trends and patterns in
traffic. We emphasize here the interplay between the Fog
and the Cloud, which must operate in mutual support.
Table1. Attribute of Smart traffic light system
Attributes of smart traffic light system
Geo-
Distribution
Wide (across region) and dense
Low
/predictable
latency
Tight within the scope of interaction.
Fog-Cloud
interplay.
Data at different time
scales(sensors/vehicles at intersection,
traffic info at diverse collection points)
Multi-
Agencies
orchestration
Agencies that run the system must co
ordinate control law policies in real
time.
Consistency Getting the traffic landscape demands
a degree of consistency between
collection of policies.
5. Consistency of a highly distributed system: Visualize the
TLS as a highly distributed collector of traffic data over an
extended geographically data. Ensuring an acceptable
degree of consistency between the different aggregator
points is crucial for the implementation of efficient traffic
policies.
6. Multi-tenancy: The Fog vision anticipates an integrated
hardware infrastructure and software platform with the
purpose of streamlining and making more efficient the
deployment of new services and applications. To run
efficiently, the Fog must support multi-tenancy. It must also
provide strict service guarantees for mission critical systems
such as the TLS, in contrast with softer guarantees for say,
infotainment, even when run for the same provider.

_______________________________________________________________________________________
7. Multiplicity of providers: The system of traffic lights may
extend beyond the borders of a single controlling authority.
The orchestration of consistent policies involving multiple
agencies is a challenge unique to Fog Computing.
4. HOW IT WILL HELP TO CONTROL THE
TRAFFIC?
Fig 1 Block diagram of Traffic light system
This system consists of a:
 Scheduler which adjust the time.
 Executor which executed the instructions taken by
the DM.
 Communicator, communicate with the neighbouring
systems as well as to the decision maker.
 Detector detects the traffic.
 Sensors helps detector to detect the abnormal
activity.
 Controller , controls the executor
 Decision Maker is nothing but the Local Server.
The above diagram shown is for the one signal similar
systems are placed on each part of the heavy traffic area.
Working
 These systems will communicate with each other
say every 15 minutes.
 The DM or the local server will communicate to
the other local server servers in every 10 minutes.
 If traffic is detected in an area, the system attached
to that area will communicate with the other systems with
the help of communicator.
 And this is how the other systems will get
information about the heavy traffic in that area.
 So the decision maker based on the data collected
from the other systems will direct the coming traffic to the
alternative road which will help to reduce the
Fig 2 Block diagram of Implementation of Traffic light
system
This was a small example of how the smart traffic system
will help to reduce the heavy traffic in that system.
The other example is related to zebra crossing:
 The sensors will detect the number of vehicles on the
zebra crossing.
 If the number of vehicles is more than the system
will not allow the pedestrians to cross the zebra
crossing unless there is a red signal.
 If the number of vehicles is less then it will give the
red signal to them and then allow the pedestrians to
cross the road.
What was the Role of FOG Computing in this
Example?
 If the decision makers were on the cloud far away
from the system location then it would have taken a
huge time in taking the decision as well as it would
cause a delay .
 Smart traffic light needs to be act in the real time.
 Therefore the Fog Computing concept resolves this
issue .
 As mentioned earlier the Fog Computing benefits
will help this Smart Traffic Light system to work
efficiently in a real time.
5. WHAT IS THE DIFFERENCE BETWEEN THE
CLOUD AND FOG?
There is ―The Ground Difference between the cloud and fog
1. Tiered organization involving multiple administrations in
the access.
2. Hierarchical control and management supporting
interplay with the Cloud
3. Expanded mobility model
4. Real-time analytics at different tiers
5. Negligible latency
6. Geo-distribution of computational power with strong
focus on service locality

_______________________________________________________________________________________
7. Orchestration involving coordinated control and actuation
in multi-tier settings
8. Unified exposure of virtualized resources (consolidated
virtualization)
9. Distributed policy exposure and policy management
involving multiple tiers.
Table 2.Difference between Cloud & Fog Computing
Requirement Cloud
Computing
Fog Computing
Latency High Low
Delay Jitter High Very low
Location of
server nodes
Within the
internet
At the edge of
the local network
Distance
between the
client and the
server.
Multiple hops One hop
Security Undefined Can be defined
Attack on data
enroute
High probability Very low
probability
Location
awareness
No Yes
Geographical
distribution.
Centralized Distributed
Number of
server nodes
Few Very large
Support for
mobility
Limited Supported
Real time
interactions
Supported Supported
Type of last mile
connectivity.
Leased line Wireless
6. CONCLUSION
Fog Computing, is a hierarchical and distributed platform
for service delivery consisting of compute, storage, and
network resources. We examined key aspects of Fog
computing, and how Fog complements and extends Cloud
computing. We looked at use cases that motivated the need
for Fog, emphasizing Fog’s relevance to several verticals
within IoT and Big Data space.
ACKNOWLEDGEMENTS
This work would not have been possible without the support
of our colleagues, and our mentor Prof.Manish Deo. I would
also like to thank my company CyberTech who helped me
to understand the concept by providing the real life
application based on Fog Computing.
REFERENCES
[1] http://www.sine-wave.com/blog/fog-computing-vs-
cloud-computing-in-the-iot#.VTsW59Kqqko ( 2014).
[2] The Fog Computing Paradigm: Scenarios and
Security Issue Ivan Stojmenovic SIT, Deakin
University, Burwood, Australia 2013.
[3] FOG Computing Mario Nemirovsky – ICREA/BSC
2014
[4] Michael Enescu - From Cloud to Fog Computing and
IoT | LinuxCon + CloudOpen North America 2014 .
[5] http://www.webopedia.com/TERM/F/fog-
computing.html

Review of implementing fog computing

Recommended

More Related Content

What's hot (20)

Viewers also liked (20)

Similar to Review of implementing fog computing (20)

More from eSAT Journals (20)

Recently uploaded (20)

Review of implementing fog computing