2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

A Design of an Adaptive Peer-to-Peer Network to Reduce Power Consumption Using Cloud Computing

Apirajitha.P.S.1 , Anitha Angayarkanni2

IpG Scholar ,CSE, Meenakshi College of Engineering Chennai, India 2Asst Prof(CSE Dept), Meenakshi College of Engineering Chennai, India

Email: lapirajitha.s@gmail.com.2nalin.vijay@gmail.com

Abstract:- Information systems based on the cloud computing

model and peer-to-peer (P2P) model are now getting popular. In the cloud computing model, a cloud of servers support thin

clients with various types of service like Web pages and

databases. On the other hand, every computer is peer and there is no centralized coordinator in the P2P model. It is

getting more significant to discuss how to reduce the total electric power consumption of computers in information

systems to realize eco-society. In this paper, we consider a

Web type of application on P2P overlay networks. A model shows how much server peer consumes electric power to

perform Web requests from client peers. Here we are creating VM migration to consolidate the server utilization. An

algorithm is used for a client peer to select a server peer in a collection of server peers so that the total power consumption can be reduced. Lastly, we evaluate the algorithms in terms of

the total power consumption and throughput compared with

traditional round robin algorithm.

IndexTerms: Distributed Computing, cloud computing,

algorithm, Peer-Peer systems, Power Consumption.

I. INTRODUCTION

Cloud computing is the delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). Cloud computing provides computation, software, data access, and storage services that do not require end-user knowledge of the physical location and configuration of the system that delivers the services. Parallels to this concept can be drawn with the electricity grid, wherein end-users consume power without needing to understand the component devices or infrastructure required to provide the service. Most cloud computing infrastructures consist of services delivered through shared data-centers and appearing as a single point of access for consumers' computing needs. Commercial offerings may be required to meet service-level agreements (SLAs), but specific terms are less often negotiated by smaller companies.

Fig. 1. Cloud architecture, the systems architecture of the software systems involved in the delivery of cloud computing, typically involves multiple cloud components communicating with each other over a loose coupling

mechanism such as a messaging queue.

ISBN No. 978-1-4673-2048-1112/$3 1.00©20 12 IEEE

Cloud In1ruh�(tut�

('9 ei!li1\9 VI''')

cloud P1.lt�i1!I

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Fig. I.Cloud computing Sample architecture

II. RELATED WORK

It is now critical to reduce the consumption of natural resources, especially petroleum. Even in information systems, we have to reduce the total electrical power consumption. We classify network applications to two types of applications, transaction and communication based ones. In this paper, we consider communication based applications like the file transfer protocol (FTP).

A computer named server consumes the electric power to transfer a file to a client depending on the transmission rate. We discuss a model for power consumption of a data transfer application which depends on the total transmission rate and number of clients to which the server concurrently transmits files. A client has to find a server in a set of servers, each of which holds a file so that the power consumption of the server is reduced. We discuss a pair of algorithms PCB (power consumption­based) [5] and TRB (transmission rate-based) [3] to find a server which transmits a file to a client. In the evaluation, we show the total power consumption can be reduced by the algorithms

III. PROPOSED S YSTEM

A. Peer-la-peer model

In this paper, we consider P2P systems where peer computers are in nature heterogeneous [2], [10] and which are fully distributed with no centralized coordinator.

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2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

Computers cannot be turned off by other persons different from the owners. A server peer is a peer which can provide other peers with some service. A client peer issues a request to a server peer. Each client peer has to find a server peer which not only satisfies service requirement but also spends less amount of electric power. There are two types of applications, transaction-based and transmission­based applications[3]. In the transaction-based applications, a client peer issues a request to a server peer and the server peer mainly consumes CPU resources to process the request, e.g. encode multimedia data in Web pages. Web applications are the typical examples. On the other hand, a server peer transmits a large volume of data to a client peer like file transfer protocol (FTP) applications. In this paper, we consider a Web application on P2P overlay networks.

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Fig-2. SYSTEM ARCHITECTURE

Fig.2 System Architecture describes the energy reduction of peer. The monitor services monitors and collects comprehensive factors such as application workload, resource utilization and Power Consumption. Migration manager [8] triggers live migration and make decision on the placement of virtual machine on physical servers based on knowledge provided by monitoring services. UI-MAP is a user interface to show the real time view of present and past system on/off status, resource consumption, and power energy consumption.

We propose a power consumption model for performing processes in a server peer. In the model, each server peer consumes maximally the electric power if at least one process is performed on the server peer. Otherwise, the server peer consumes minimum electric power. A request to perform a process like a Web page request is sent to one of the server peers. A client peer selects one server peer in a set of the server peers. We propose two types of algorithms, Laxity based (LB) algorithm and Total power

consumption laxity based (TPCLB) algorithm to select a server peer in a collection of server peers so as to not only satisfy deadline but also to reduce power consumption.

We evaluate the LB and TPCLB algorithms in terms of computation time and electric power consumption compared with the basic round robin (RR) algorithms [6] in the simulation using cloudSim. We show how the total power consumption can be reduced in the LB and TPCLB algorithms compared with the RR algorithm [7].

IV. SIMPLE COMPUT A nON MODEL

A process being performed at time t is current at time t. A process which already terminates before time t is previous at time t. Let CP(t) be a set of current processes on a server peer C, at time t. N,(t) shows the number of current processes, N.(t)= 1 C P i (t) I. If CP,(t)={ps} and N.(t)=l , a

process Ps is exclusively performed on a server peer c, at

time t . If Ps E CPi(t) and N.(t»l , a process Ps is

interleaved with another process Pu E CP,(t) (t:;t: u), i.e.

multiple processes are being performed on a server peer c, at time t. {u,(t) • max Fis(t)/max Fs } fis(t) =

ai(t) • min Fis(t) / minFs (I)

Suppose a process ps is exclusively performed on the

fastest server C and slower server Cj Here, w(t)=u;(t) = 1. fis(t)=I.

butfis(t)=max FiJmax Fs. The maximum NRC flS(t)

shows:- max Fis/max Fs,O :s; fis(t):s; max Fis :s; 1.fis(t)

maxfis=l -.------------.-----------.---.----------------.-.

maxis '-r--�-...,.--.----------------.-'

stjs

I ,

-_. , , , , , I r--"

I

I __ J

I , , I.. _ _ I

, , , �-.. --:

! 1. _____ ..

I

! I

etjs et'js

Fig.3. Normalized Computation Rate

Fig.3 shows how many numbers of steps of a process ps are performed on a server C at time t

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2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

A. Comparison of RR and LB of peer server

Table1.Energy consumption of peer server ofRR algorithm and LB algorithm

Number Simulation Energy

Port of host

Time Consumed server

connected runs

100 12280.00 248.41 125

200 12000.00 248.08 125

300 11960.00 247.12 80

400 12060.00 239.45 124

The tablel describes the comparison simulation of RR algorithm and Lb algorithm of peer server connected to various hosts.

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! �400 �300 !200 f-• �IOO r---

,L

_cloud -I{ __ _

__Traditiooal

o lOCI ISO 200 250 300 350

E""ryComumplion

Graph.1 Comparison of Peer server and RR

The graph. 1 shows the comparison of power consumption between the server and the e1ient using RR algorithm and LB algorithm in various steps

V.SERVER SELECTION ALGORITHM

A. LB Algorithm

LB(t,C,Ps)

{

CI,=null;

for each server c, in C{

<EttCt),Etps(t»=Estimate Termination(INTi(t),KPtCt),ps);

e15(t)=Etps(t)-t;

CLi=CLi+{ e15(t)} ;

Server=ci where e11S(t) is min in CL,

Return (server) ;}

The computation laxity (CL) e11S(t) of a process Ps shows how long it takes to perform up the process Ps from time t on a server C, . Suppose a process Ps is issued to the load

balancer k at time t. The CL of a process on each server C, is given as follows:

CI5(t)=ETps(t)-t (2) In the LB algorithm, a server which can most early terminate a process is selected for the process Ps. A server C, is selected for the process Ps at time t by the LB algorithm. Where the algorithm comprises:-INT,(t) --Set of time intervals; KP,(t) -- current Knot; ETtCt) - Estimate Termination; C - Set of servers;

Table2 Selection of Servers Server C1 C2 C4 C4 C5

Max fi 1.000 0.998 0.850 0.840 0.839

L, 0.98 0.98 0.96 0.96 0.95

Min 149 141 133 128 108 E, Max 197 186 160 156 138 E,

The Table2 describes the selection of server c, among the set of servers C, they are ordered by the speed of the CPU and the response time. The Servers are ordered with respect to processing speed, i.e maximum computation rate. The min PCR is randomly selected between 85 and 150[W/tu]. The max NCR is selected from 1 and 0.7 [t/u]. The degradation rate is selected from 0.99 and 0.95.

B. TPCLB Algorithm

The power consumption laxity (PCL) letCt) of each server c, is estimated at time t. The PCL letCt) is given as follows

letCt)=max. etCEttCt)-t) (3)

In the TPCLB algorithm, a server C, which consumes the minimum power consumption is selected for a process Ps, i.e. Equation (3) provides a server c, whose PCL letCt) is minimum is selected in the server set c. At time t, a server c, is selected for process Ps by the following procedure

TPCLB(t,C,ps) {

Lei=� For each server c, in C

{ Calculate estimate termination of process letCt)=max. etCEttCt)-t); Le,=Le,+{letCt)} ;

} Server=c, where letCt) is minimum in Le, Return (server); }

200

2012 IEEE International Conference on Advanced Communication Control and Computing Technologies (ICACCCT)

325

320

315

� 310

.5 e- 305 • 8. � 300

295

290

285 "b1 "b2 "b3

• RR

.ClB

_pelB

Fig.5. Performance Analysis of RR, LB, TPCLB algorithms.

The above figure.S represents the performance of the three algorithms and their result of response time shown in x axis, has been given in hrs and the user bases are shown in y axis. Therefore the LB algorithm decreases by 9% and the TPCLB algorithm reduces by 6.9% of electric consumption in peer servers.

VI. CONCLUSION

In this project the future enhancement can be processed through extending the algorithms, and comparing with the response time including environment variables like meter reading, along with their deadlines. The process can be established through making the algorithm easier and more consumable. The server peer and client peer simulation energy can be implemented using TPCLB algorithm, where these two LB and TPCLB are compared with the existing Round Robin algorithm, which provides better result of showing power consumption. Therefore TPCLB is consuming less power than LB and RR algorithms in peer-to-peer network. The TPCLB algorithm reduces by 6.9% of consumption

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