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Overload Avoidance Model using Optimal Placement of Virtual Machines in Cloud Data Cetres

by Narander Kumar, Shalini Agarwal, Vipin Saxena
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 11
Year of Publication: 2013
Authors: Narander Kumar, Shalini Agarwal, Vipin Saxena
10.5120/12786-9963

Narander Kumar, Shalini Agarwal, Vipin Saxena . Overload Avoidance Model using Optimal Placement of Virtual Machines in Cloud Data Cetres. International Journal of Computer Applications. 73, 11 ( July 2013), 18-25. DOI=10.5120/12786-9963

@article{ 10.5120/12786-9963,
author = { Narander Kumar, Shalini Agarwal, Vipin Saxena },
title = { Overload Avoidance Model using Optimal Placement of Virtual Machines in Cloud Data Cetres },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 11 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number11/12786-9963/ },
doi = { 10.5120/12786-9963 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:39:49.641253+05:30
%A Narander Kumar
%A Shalini Agarwal
%A Vipin Saxena
%T Overload Avoidance Model using Optimal Placement of Virtual Machines in Cloud Data Cetres
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 11
%P 18-25
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Cloud data centres improve CPU utilization of their servers (physical machines or PMs) through Virtualization (virtual machines or VMs). Over virtualised and under virtualized PMs suffer performance degradation and power dissipation respectively. This work presents a stochastic modular scheme for allocating VM requests to a PM by avoiding overloading of PM and keeping the global load characteristics under specified QoS goal. The proposed approach categorizes PMs into three groups (Under Load, Normal Load, Over Load) in a way that minimizes number of PMs in Under Load and Over Load groups and maximizes number of PMs in Normal Load group. We compute VM request rejection probability, response time, service time and number of PMs that are overload or under loaded for evaluating the performance of our model. The results show that these parameters do not degrade with increasing arrival rate. Thus the proposed model is simple yet efficient approach for VM placement problem.

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Index Terms

Computer Science
Information Sciences

Keywords

Markov Chain Virtual Machine Physical Machine Virtual Chunks VM Consolidation

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