Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSingh J-
dc.contributor.authorSidhu J.-
dc.description.abstractVirtual machine consolidation is a major solution for addressing the issue of increasing energy consumption by cloud computing data centers. A lot of work is done on developing algorithms for detecting underloaded, overloaded hosts, selection of virtual machines and their placement to perform the consolidation. These algorithms are usually tested on publicly available Planet lab workload. There is a need to know how benchmarks algorithms used in consolidation of virtual machines respond to other workloads. This paper is an attempt to evaluate these algorithms on Google workload trace. An importer is made to use this dataset by extending the CloudSim toolkit. The comparison of results using Planet lab and Google workload traces is made which shows the difference of 46.41%, 14.84%, 12.86% and 44.83% respectively in terms of number of virtual machine migrations, service level agreement violation time per active host, number of hosts shutdown, and energy consumption. The objective comparison of results illustrated that there is a need to test the proposed algorithms on multiple datasets in order to be assessed as optimal. � 2020 The Authors. Published by Elsevier B.V.en_US
dc.publisherElsevier B.V.en_US
dc.subjectcomparative analysisen_US
dc.subjectenergy consumptionen_US
dc.subjectGoogle cluster traceen_US
dc.subjectPlanet lab workloaen_US
dc.subjectdVM consolidation algorithmsen_US
dc.titleComparative analysis of VM consolidation algorithms for cloud computingen_US
Appears in Collections:Conferences

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.