This document discusses a proposed framework for green computing networks. It begins with an introduction that outlines the energy crisis and impact of increased connectivity on the environment. It then reviews existing solutions for wireless networks, including caching, virtualization, network services, energy awareness, and cloud computing. The document proposes an architecture for green computing networks that utilizes software-defined networking and information-centric networking principles. It leverages concepts like caching, virtualization and energy-aware algorithms to more efficiently schedule tasks based on available energy. The goal is to minimize the environmental impact of rapidly growing wireless networks through this software-based approach.

1. TABLE OF CONTENTS:
1. INTRODUCTION.............................................................................................................2
1.1 Energy Crisis ................................................................................................................3
1.2 Current status of devices................................................................................................4
1.3 Industrial View .............................................................................................................4
1.4 Impact of Change..........................................................................................................4
2. LITERATURE REVIEW .................................................................................................5
2.1 Wireless computing networks.........................................................................................5
2.2 Solutions developed which are also used in the proposed framework architecture ..............6
2.2.1 Caching..................................................................................................................6
2.2.2 Virtualization..........................................................................................................6
2.2.3 Network & Computing Services (NCS) ....................................................................6
2.2.4 Energy Awareness...................................................................................................7
2.2.5 Cloud Services........................................................................................................7
3. GREEN COMPUTING NETWORKS..............................................................................8
3.1 Basic terminologies .......................................................................................................8
3.1.1 DENS.....................................................................................................................8
3.1.2 ICN........................................................................................................................9
3.3 Need of Green Computing ...........................................................................................10
4. ARCHITECTURE..........................................................................................................11
5. ALGORITHM ................................................................................................................13
6. APPLICATIONS............................................................................................................16
7. ADVANTAGES..............................................................................................................17
8. DISADVANTAGES........................................................................................................18
9. SCOPES FOR FUTURE DEVELOPMENT ...................................................................19

2. CHAPTER 1
1. INTRODUCTION
Energy crisis has been largely talked of, also various measures to ensure that we don’t
run of out power have been adopted worldwide but still most areas of the world don’t
have uninterrupted supply today. The future scenario is even worse. It is the need of the
hour to encourage people to use and develop systems which don’t have an impact on the
globe directly or indirectly.
The most significant part in evolution of modern computers from the 20th to the 21st
century can be marked by the developments in networking. The rise of the Internet
through World Wide Web (WWW) caused a boom in the computer networks sector.
Further, today the focus is rather shifted more towards wireless networking with
technologies like Wi-Fi, Bluetooth, ZigBee being on the rise due to its numerous
advantages. During all these developments various applications based on computer
systems were designed, and most of them have integrated in modern lifestyle as without
it live would be tough. Social media, Digital banking being the best example of the same.
But one area which was left rather uncovered is the impact of this rapid development of
our environment. During projects the cost and risks are estimated and the project is
optimized accordingly. But seldom do we consider effects of our systems on nature. If we
break the computer system into modules like display, power supply, networking, input
output peripherals, etc. only the networking modules needs consent interaction with the
outside world which is possible only with networking devices. Also in mobiles and
embedded systems the power supply is extremely limited and these devices are constantly
connected to different devices through the Internet. These areas of concern are discussed
in this seminar and the best possible ways and approaches to solve these are highlighted.

3. And if the networking world is broadly classified further as hardware components and
software components, we observe that the hardware consumption is easy to track and
necessary actions can be taken by manufactures, having said this digital waste is now a
priority issue being addressed in most developed countries and constant research is going
in the this field . This narrows the problem left due to software and algorithms used in
networking applications. Here we discuss the solutions to the above discussed problem
that is minimizing effects of networking on environment through software based
networking.
The concept of Green computing for evolved for the solution of the same and is under
developments. Various aspects of Green computing have been discussed in the following
content. But before that the need of developing such systems and related content is
discussed.
1.1 Energy Crisis
The future global economy is likely to consume ever more energy, especially with the
rising energy demand in developing countries such as India and China. At the same time,
the tremendous risk of climate change associated with the use of fossil fuels makes
supplying this energy increasingly difficult.
The potential for crisis if we run out of energy is very real but there is still time before
that occurs. At expected rates of demand growth we have enough for thirty years supply
[1], the limited supply potential of non-renewable energy sources cannot ensure that the
world does not fall short of its energy needs. Global warming is been on the rise and the
infinite servers of various organizations are contributes to it too. Also data replication and
need of continuous power supply of these servers are also not having positive impact on
the environment.[2]

4. 1.2 Current status of devices
According to estimation the world population will be around 8 billion in 2017
compared to the number of devices connected to Internet will be up to 24 billion. Internet
usage means use of networking services. [3]
Because mobile devices are dependent on battery power, it is important to minimize
their energy consumption. The energy consumption of the network interface can be
significant, especially for smaller devices. Most research in energy conservation
strategies has targeted wireless networks that are structured around base stations and
centralized servers, which do not have the limitations associated with small, portable
devices.
1.3 Industrial View
The major issue which is being focused in almost all the industries is the energy
consumption issue. The increase in energy consumption results in many problems related
to the environment. One of these problems include the emission of Green House Gases
(GHG).[4] During the past few years, the emission of Green House Gases has increased
exponentially and it has had a destructive effect on the atmosphere. Even computer
systems have a carbon footprint and heating emissions have been related to them. The
proposed framework aims for faster information retrieval there by needing less
computational power to avoid the heating effects.
1.4 Impact of Change
As stated above about 24 billion devices will be connected to the Internet by end of
2017 so any positive change bought about in networking will have a huge impact overall.
The solutions discussed are software related so there will not be any need to change any

5. hardware and they will not be restricted specific hardware configurations as software can
be remodeled for different systems.
CHAPTER 2
2. LITERATURE REVIEW
Various terms defined and involved in existing solutions to Green computing
networks are discussed.
2.1 Wireless computing networks
Wireless networks are computer networks that are not connected by cables of any
kind. The use of a wireless network enables enterprises to avoid the costly process of
introducing cables into buildings or as a connection between different equipment
locations. The basis of wireless systems are radio waves, an implementation that takes
place at the physical level of network structure.
Wireless networks use radio waves to connect devices such as laptops to the Internet,
the business network and applications.[2] When laptops are connected to Wi-Fi hot spots
in public places, the connection is established to that business’s wireless network. The
smartphone boom has been a major contributing factor to need of wireless computing
networks.
There are four main types of wireless networks:
 Wireless Local Area Network (LAN): Links two or more devices using a wireless
distribution method, providing a connection through access points to the wider
Internet.
 Wireless Metropolitan Area Networks (MAN): Connects several wireless LANs.
 Wireless Wide Area Network (WAN): Covers large areas such as neighboring
towns and cities.

6.  Wireless Personal Area Network (PAN): Interconnects devices in a short span,
generally within a person’s reach.
2.2 Solutions developed which are also used in the proposed framework architecture
2.2.1 Caching
Network caching is the technique of keeping frequently accessed information in a
location close to the requester.[4] A Web cache stores Web pages and content on a
storage device that is physically or logically closer to the user-closer and faster than a
Web lookup. Similarly data caches are also present.
2.2.2 Virtualization
Network virtualization refers to the management and monitoring of an entire computer
network as a single administrative entity from a single software-based administrator’s
console.[7] Network virtualization also may include storage virtualization, which
involves managing all storage as a single resource. Network virtualization is designed to
allow network optimization of data transfer rates, flexibility, scalability, reliability and
security. It automates many network administrative tasks, which actually disguise a
network's true complexity. All network servers and services are considered one pool of
resources, which may be used without regard to the physical components.
Network virtualization is especially useful for networks experiencing a rapid, large
and unpredictable increase in usage. The intended result of network virtualization is
improved network productivity and efficiency, as well as simplifying work for the
network administrator.
2.2.3 Network & Computing Services (NCS)
NCS provides computer/network technical support and is committed to delivering
secure, responsive, high-quality, customer-oriented services and support that foster a
productive system.[11]

7. NCS achieves this mission by incorporating innovative technology products from the
private sector with the highest-quality products and services developed internally. This
cost-effective and balanced technology helps to ensure that the users enjoy a solid
technological infrastructure, reliable critical services and customer-focused support
systems to meet needs of today and tomorrow.
2.2.4 Energy Awareness
An energy aware system as the name suggests is always aware of amount of energy in
the same system.[9] These systems are scheduled on basis on amount of power supply
left. Hence we use energy aware algorithms in such systems as standard algorithms are
inefficient. High importance applications are always based on this standard. DENS is
one of the most popular algorithm available.
2.2.5 Cloud Services
Cloud computing is a type of Internet-based computing that provides shared computer
processing resources and data to computers and other devices on demand.[12] It is a
model for enabling ubiquitous, on-demand access to a shared pool of configurable
computing resources (e.g., computer networks, servers, storage, applications and
services), which can be rapidly provisioned and released with minimal management
effort. Cloud computing and storage solutions provide users and enterprises with various
capabilities to store and process their data in either privately owned, or third-party data
centers that may be located far from the user–ranging in distance from across a city to
across the world. Cloud computing relies on sharing of resources to achieve coherence
and economy of scale, similar to a utility (e.g., like the electricity grid over an electricity
network).

8. CHAPTER 3
3. GREEN COMPUTINGNETWORKS
Green wireless computing requires the in depth study of networking caching and
computing. It is basically aimed at reducing energy consumption of the system. With the
developments in technology all these have been studied individually to a very large
extent. A new concept of SDN came into being with it. [5]
Green computing also uses cloud computing but cloud computing but is not fully
developed yet. Cloud computing is not cost effective and environment friendly when
considered minutely.
3.1 Basic terminologies
Some basic terms used in Green computing are discussed
3.1.1 DENS
It’s a fact that each datacenter comprises of thousands of physical machines running
millions of Virtual machines and arranged in massive racks. It’s natural that this will
consume huge amounts of energy. For this the Datacenter Energy-efficient Network-
aware Scheduling algorithm (DENS) is proposed.
The DENS methodology minimizes the total energy consumption of a data center
by selecting the best-fit computing resources for job execution based on the load level
and communication potential of data center components [10]. The communicational
potential is defined as the amount of end-to-end bandwidth provided to individual servers
or group of servers by the data center architecture. Contrary to traditional scheduling
solutions that model data centers as a homogeneous pool of computing servers, the
DENS methodology develops a hierarchical model consistent with the state of the art data
center topologies.

9. 3.1.2 SDN
Software-defined networking (SDN) is an umbrella term encompassing several kinds
of network technology aimed at making the network as agile and flexible as the
virtualized server and storage infrastructure of the modern data center. The goal of SDN
is to allow network engineers and administrators to respond quickly to changing business
requirements. [1]
In a software-defined network, a network administrator can shape traffic from a
centralized control console without having to touch individual switches, and can deliver
services to wherever they are needed in the network, without regard to what specific
devices a server or other hardware components are connected to. [8] The key
technologies for SDN implementation are functional separation, network virtualization
and automation through programmability
Software Defined Networking describes how the network can be programmed via a
logically software defined controller and separate the control from the data. [6] The
framework of SDN will be elaborated further. If the wireless networks are software
defined then it means that the wireless network connections are directly enabled and hide
the underlying infrastructure for applications in green wireless network management.
3.1.2 ICN
Information-centric networking (ICN) is an approach to evolve the Internet infrastructure
away from a host-centric paradigm based on perpetual connectivity and the end-to-end
principle, to a network architecture in which the focal point is “named information” (or
content or data). In this paradigm, connectivity may well be intermittent, end-host and in-
network storage can be capitalized upon transparently, as bits in the network and on
storage devices have exactly the same value, mobility and multi access are the norm and
anycast, multicast, and broadcast are natively supported. Data becomes independent from
location, application, storage, and means of transportation, enabling in-network caching
and replication. The expected benefits are improved efficiency, better scalability with
respect to information/bandwidth demand and better robustness in challenging
communication scenarios.

10. 3.2 Evolution of Green Computing Networks
The term green computing is not yet very well defined technically, so any technology
which is more energy efficient can be deemed into this category. It is very difficult to
classify in other ways.
Green Computing Networks started with introduction of caching in networks, later
new algorithms started to be written and this led to Software Defined Networking
furthermore recently energy aware systems are being employed. While Artificial
Intelligence can change the networking system by data mining to have better caching,
less congestion and efficient scheduling
3.3 Needof Green Computing Networking
The impact of green networks is endless as huge datacenters consume electricity
almost equal to normal public usage, while people still don’t have power supply in all
parts of the world these datacenters eat up a massive amount of energy.[8]
Following graph shows the impact of green computing in networks.
Figure 3.1: Impact of Green Computing Networks[1]

11. CHAPTER 4
4. ARCHITECTURE
Figure 4.1: Architecture of proposed framework[1]

12. At the top of architecture there are network operating systems which consists of actual
data and we implement various routing and scheduling algorithms, the approach of SDN
which is defined above is used here. The approach is completely software centric which
makes the system more flexible, a generic approach is not followed
The switch hypervisor mainly implements and administrates the communication
between switches and controller. The network hypervisor is used to monitor the
networking status, such as congestion. The topology hypervisor masters all the physical
nodes, links, and ports through regular monitoring. These hypervisors will map the
abstracted resource slices to the physical infrastructure. Based on the information
mastered by these hypervisors, the controller could implement some operations or
strategies from the network applications layer, and ensure the isolation. Furthermore, the
controller could guide packet forwarding of the devices in data plane, as well as perform
the commands of communicating, computing, and accessing according to these
information lists.
The switch hypervisor is connected is connected to Heterogeneous wireless network
where there are different wireless devices like WiFi, routers which intercommunicate
using various gateway at every unit there is a cache memory and virtualization is allowed
at every stage to make sure no system is overloaded.

13. CHAPTER 5
5. ALGORITHM
The standard algorithms for scheduling are well known and now being from a long
time to date in numerous applications. These algorithms are to be modified so as to more
energy efficient while not compromising on the throughput of the system.
 First Come First Serve
First come, first served (FCFS) is an operating system process scheduling algorithm
and a network routing management mechanism that automatically executes queued
requests and processes by the order of their arrival. With first come, first served, what
comes first is handled first; the next request in line will be executed once the one before it
is complete.
The proposed modification is that in preemptive scheduling FCFS also keeps track of
tasks, if a task need 10 units of energy while the system has less than 10 units remaining
there is no point in scheduling that task as it will to failure eventually.
 Round-Robin
Round robin scheduling (RRS) is a job-scheduling algorithm that is considered to be
very fair, as it uses time slices that are assigned to each process in the queue or line. Each
process is then allowed to use the CPU for a given amount of time, and if it does not
finish within the allotted time, it is preempted and then moved at the back of the line so
that the next process in line is able to use the CPU for the same amount of time.
As this scheduling has free states it is not considered efficient the proposed solution is
to assign processors to tasks which consume energy proportional to the system. Example:
The system is charging at 5 units per minute and when is charged 10 units a task T1
enters the ready queue. T1 needs 10 units per minute power for 3 minutes. In this case if
the scheduler directly assigns the processor without checking power needs the system
will fail. While when the processor is free it must go to power saving modes.

14.  Min-Min Algorithm
Min-Min algorithm schedules the task which has minimum of the parameters under
consideration. The Min Min algorithm computes the solution with limited resources and
in minimal cost.
 Max-Min Algorithm
Max-Min algorithm is quite similar to Min-Min algorithm except for in this case we
have one attribute which does not cause an impact on the efficiency and it is having a
higher respective value . For example, a system may have a very high computing
processor and the system is developed for basic operations. So the algorithm used in this
case does not need to worry about the processing time needed .Also the algorithm must
not context switch much as it would hardly make a difference to the system efficiency.
The Min-Min and Max-Min algorithms are oriented according to systems and hence
cannot be implemented directly before analyzing the system.
 Swarm Optimization
In networking, particle swarm optimization (PSO) is a computational method that
optimizes a problem by iteratively trying to improve a candidate solution with regard to a
given measure of quality. It solves a problem by having a population of candidate
solutions, here dubbed particles, and moving these particles around in the search-space
according to simple mathematical formulae over the particle's position and velocity. Each
particle's movement is influenced by its local best known position, but is also guided
toward the best known positions in the search-space, which are updated as better
positions are found by other particles. This is expected to move the swarm toward the
best solutions.
In case of networking instead of distance we check for energy consumption to make
the system energy efficient.

16. CHAPTER 6
6. APPLICATIONS
1. In software industries.
Each software organization has its own database stored on either the Cloud or
some private datacenter. These systems could be made better by applying this
framework.
2. In making public systems more energy efficient
As discussed networking is a basic computing element as people would be
volunteering to upgrade their system at a very low cost. Also it will help in cost
saving the long run.
3. In embedded systems
Embedded systems have the biggest limitation in power supply also all
networking here is wireless. Power consumption matters a lot in such appliances
which would be reduced.
4. IOT based applications.
IOT is defined as a network of devices. Network connectivity of all nodes is
required throughout the application also most IOT components are battery based
and need to be charged if power is drained.
5. Datacenters where servers are powered on all time.
The biggest impact of this framework has to on Daacenters where huge
amount of power is continuously consumed. Even small savings of Energy here
would have a huge impact overall.

17. CHAPTER 7
7. ADVANTAGES
 Software-defined networking
The control function is no longer confined to routers, or programmed and defined only by
the manufacturers of equipment. Therefore, SDN achieves better flexibility and
controllability.
 Information-centric networking
Popular contents are transmitted repeatedly on the Internet, wasting resources and
reducing quality of service (QoS).
 Energy efficient coding
The principle behind energy efficient coding is to save power by getting software to make
less use of the hardware, rather than continuing to run the same code on hardware that
uses less power.
 Improved repair, re-use, recycling and disposal
Popular contents are transmitted repeatedly on the Internet, wasting resources and
reducing quality of service (QoS).

18. CHAPTER 8
8. DISADVANTAGES
 To achieve scalability
The framework discussed uses software defined networking approach centrally manage
and control Networking, caching, and computing resources. Since there are various
access
Devices, gateway devices, and network nodes in heterogeneous wireless networks, the
controller has to maintain a large central database
 In developing new Resource allocation strategies
Resources are the most important aspect in SDN , they include Networking, caching, and
computing resources .Therefore, it is important to design the Resource allocation
strategies to make a tradeoff between the deployment and operation costs(e.g., energy
consumption) and performance benefits (e.g., decreasing latency).
 Security
If attacked software could be a single point of failure resulting in the attacker getting all
permissions to modify systems. DOS attacks could be carried out using dummy nodes
disguised as routers, hubs, etc. So the system must be designed in a way that it is attack
tolerant. It is recommended that the system uses some kind of encryption.
 Cooperation incentives among stakeholders
As we jointly consider networking, caching, and computing techniques in our proposed
framework, it is nontrivial to develop this framework in practice. It is possible that
Internet service Providers (ISPs) will take the responsibility to develop this framework
due to the improved user Experience and energy efficiency. Nevertheless, it is a
significant challenge for ISPs to develop this Framework.

19. CHAPTER 9
9. SCOPES FOR FUTURE DEVELOPMENT
 To develop the proposed framework in an optimal way.
The framework is yet to be developed which makes it vulnerable to design issues
faced at time of development. Also only simulation results are available now which
are not always accurate.
 To develop algorithms which can perform scheduling in a better way.
Here we discuss only the basic algorithms but better algorithms for more powerful
systems need to be developed accordingly.
 Expanding the framework from networking to the other parts of the system.
The software centric approach can be used in basic OS operations as well saving
the systems need. But this must not affect computational power of the system or
introduce delays.
 Replace existing systems.
The current systems must be upgraded with this framework this should not be a
major issue at the client level but vast changes need to be addressed at the server
level.
 Power off replicated servers alternatively.
A task in addition to the current framework will be developing a algorithms which
can power off the replicated data as when the main system is working well this
replicated server is on without purpose.

20. CONCLUSION
In this seminar, recent advances in networking, caching, and computing have been
reviewed. I propose to integrate networking, caching, and computing in a systematic
framework for next generation green wireless networks. The architecture of the proposed
framework is developed by software defined networking, caching, and computing.
Details in its key components of data, control, and management planes are specified.
Some expected results have been shown to assure that this proposed framework can
improve users’ experience and energy efficiency. In addition, some open research
challenges including scalable controller design, networking/ caching/computing resources
allocation strategies, and security issues are also mentioned. Future work is expected to
be in progress to address these research challenges. Finally a revolutionary change in the
way of computing networks is desired.

21. REFERENCES
[1]. Ru Huo, Fei Richard Yu, Tao Huang, Renchao Xie, Jiang Liu, Victor C.M. Leung,
and Yunjie Liu,” Software Defined Networking, Caching, and Computing for Green
Wireless Networks, IEEE Communications Magazine November 2016”
[2]. Shivam Singh, “Green Computing Strategies & Challenges”, 2015 International
Conference on Green Computing and Internet of Things (ICGCIoT) ,Pg.758-760
[3]. Shaden M. AlIsmail, Heba A. Kurdi, “Green Algorithm to Reduce the Energy
Consumption in Cloud Computing Data Centres”, SAI Computing Conference 2016 July
13-15, 2016, London, UK
[4]. Rubyga. G1, Dr. Ponsy R.K Sathia Bhama ,” A Survey Of Computing Strategies For
Green Cloud”, 2016 Second International Conference on Science Technology
Engineering and Management (ICONSTEM)
[5]. Yahav Biran.” Coordinating Green Clouds as Data-Intensive Computing”, 2016
IEEE Green Technologies Conference
[6]. Bruno Astuto A. Nunes, Marc Mendonca, Xuan-Nam Nguyen, Katia Obraczka, and
Thierry Turletti, “A Survey of Software-Defined Networking: Past, Present, and Future
of Programmable Networks”, IEEE COMMUNICATIONS SURVEYS &
TUTORIALS,2014
[7]. Durga Chowdary E, Neelam R Vaishnav, G. Apoorva ,“Green Networking using a
combination of network virtualization and adaptive link rate”, IEEE International
Conference On Recent Trends In Electronics Information Communication Technology,
2015

22. [8]. Chao Qiu, Tiehong Tian, “Multiple Controllers Sleeping Management in Green
Software Defined Wireless Networking”, IEEE ICT conference, 2016
[9]. Jayant Adhikari, Prof. Sulabha Patil, “Comparison of Energy Aware Load Balancing
Algorithms in Cloud Computing”, International Journal of Scientific & Engineering
Research, Volume 4, Issue 12, December-2013
[10]. Dzmitry Kliazovich, Pascal Bouvry, Samee Ullah Khan, “DENS: Data Center
Energy-Efficient Network-Aware Scheduling”, Green Computing and Communications
(GreenCom), 2010 IEEE
[11]. Muhammad Ismail, Muhammad Zeeshan Shakir, Khalid A. Qaraqe, Erchin
Serpedin, “Green Network Solutions”, IEEE Green Heterogeneous Wireless
Networks,2016
[12]. Guangjie Han; Jinfang Jiang; Mohsen Guizani; Joel J. P. C Rodrigues, “Green
Routing Protocols for Wireless Multimedia Sensor Networks”, IEEE Wireless
Communications, 2016