A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing
Summary The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agree...
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| Veröffentlicht in: | International journal of communication systems 2018-05, Vol.31 (8), p.n/a |
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| container_title | International journal of communication systems |
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| creator | Venkata Krishna, J. Apparao Naidu, G. Upadhayaya, Niraj |
| description | Summary
The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.
This paper presents a virtual machine migration algorithm based on Lion‐Whale optimization by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale virtual machine migration based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. |
| doi_str_mv | 10.1002/dac.3539 |
| format | Article |
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The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.
This paper presents a virtual machine migration algorithm based on Lion‐Whale optimization by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale virtual machine migration based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit.</description><identifier>ISSN: 1074-5351</identifier><identifier>EISSN: 1099-1131</identifier><identifier>DOI: 10.1002/dac.3539</identifier><language>eng</language><publisher>Chichester: Wiley Subscription Services, Inc</publisher><subject>Cloud computing ; Computer simulation ; Computing costs ; Data centers ; Energy consumption ; Experimentation ; Fitness ; Genetic algorithms ; Lion optimization algorithm ; Optimization algorithms ; Particle swarm optimization ; Placement ; Quality of service architectures ; Virtual environments ; virtual machine migration ; Whale optimization algorithm</subject><ispartof>International journal of communication systems, 2018-05, Vol.31 (8), p.n/a</ispartof><rights>Copyright © 2018 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2939-a347f793f1d3f3572c521ca45a4840765cfa09c839f56d5790c032bd286ff1533</citedby><cites>FETCH-LOGICAL-c2939-a347f793f1d3f3572c521ca45a4840765cfa09c839f56d5790c032bd286ff1533</cites><orcidid>0000-0002-8562-3594</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fdac.3539$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fdac.3539$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids></links><search><creatorcontrib>Venkata Krishna, J.</creatorcontrib><creatorcontrib>Apparao Naidu, G.</creatorcontrib><creatorcontrib>Upadhayaya, Niraj</creatorcontrib><title>A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing</title><title>International journal of communication systems</title><description>Summary
The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.
This paper presents a virtual machine migration algorithm based on Lion‐Whale optimization by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale virtual machine migration based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit.</description><subject>Cloud computing</subject><subject>Computer simulation</subject><subject>Computing costs</subject><subject>Data centers</subject><subject>Energy consumption</subject><subject>Experimentation</subject><subject>Fitness</subject><subject>Genetic algorithms</subject><subject>Lion optimization algorithm</subject><subject>Optimization algorithms</subject><subject>Particle swarm optimization</subject><subject>Placement</subject><subject>Quality of service architectures</subject><subject>Virtual environments</subject><subject>virtual machine migration</subject><subject>Whale optimization algorithm</subject><issn>1074-5351</issn><issn>1099-1131</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp10MtKxDAUBuAgCuoo-AgBN2465tI0zXIYrzDgRnEZMrnMZGibmrTKuPIRfEafxM7Uratz-Pk4B34ALjCaYoTItVF6ShkVB-AEIyEyjCk-3O08zxhl-BicprRBCJWkYCfAzuDCh-bn6_t1rSoLQ9v52n-qbgyXKlkDa7-K-wQGB9997HpVwVrptW9sgi5EaFSnoLZNZ2OCvoG6Cr2BOtRt3_lmdQaOnKqSPf-bE_Byd_s8f8gWT_eP89ki00RQkSmac8cFddhQRxknmhGsVc5UXuaIF0w7hYQuqXCsMIwLpBElS0PKwjnMKJ2Ay_FuG8Nbb1MnN6GPzfBSEkRKjlEp2KCuRqVjSClaJ9voaxW3EiO5K1EOJcpdiQPNRvrhK7v918mb2XzvfwFn73Rm</recordid><startdate>20180525</startdate><enddate>20180525</enddate><creator>Venkata Krishna, J.</creator><creator>Apparao Naidu, G.</creator><creator>Upadhayaya, Niraj</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-8562-3594</orcidid></search><sort><creationdate>20180525</creationdate><title>A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing</title><author>Venkata Krishna, J. ; Apparao Naidu, G. ; Upadhayaya, Niraj</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2939-a347f793f1d3f3572c521ca45a4840765cfa09c839f56d5790c032bd286ff1533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Cloud computing</topic><topic>Computer simulation</topic><topic>Computing costs</topic><topic>Data centers</topic><topic>Energy consumption</topic><topic>Experimentation</topic><topic>Fitness</topic><topic>Genetic algorithms</topic><topic>Lion optimization algorithm</topic><topic>Optimization algorithms</topic><topic>Particle swarm optimization</topic><topic>Placement</topic><topic>Quality of service architectures</topic><topic>Virtual environments</topic><topic>virtual machine migration</topic><topic>Whale optimization algorithm</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Venkata Krishna, J.</creatorcontrib><creatorcontrib>Apparao Naidu, G.</creatorcontrib><creatorcontrib>Upadhayaya, Niraj</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>International journal of communication systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Venkata Krishna, J.</au><au>Apparao Naidu, G.</au><au>Upadhayaya, Niraj</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing</atitle><jtitle>International journal of communication systems</jtitle><date>2018-05-25</date><risdate>2018</risdate><volume>31</volume><issue>8</issue><epage>n/a</epage><issn>1074-5351</issn><eissn>1099-1131</eissn><abstract>Summary
The scalability, reliability, and flexibility in the cloud computing services are the obligations in the growing demand of computation power. To sustain the scalability, a proper virtual machine migration (VMM) approach is needed with apt balance on quality of service and service‐level agreement violation. In this paper, a novel VMM algorithm based on Lion‐Whale optimization is developed by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale VMM based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit. The performance of the proposed method is validated over existing optimization‐based VMM algorithms, such as particle swarm optimization and genetic algorithm, using the performance measures, such as energy consumption, migration cost, and resource use. Simulation results reveal the fact that the proposed Lion‐Whale VMM effectively outperforms other existing approaches in optimal VM placement for cloud computing environment with reduced migration cost of 0.01, maximal resource use of 0.36, and minimal energy consumption of 0.09.
This paper presents a virtual machine migration algorithm based on Lion‐Whale optimization by integrating the Lion optimization algorithm and the Whale optimization algorithm. The optimal virtual machine (VM) migration is performed by the Lion‐Whale virtual machine migration based on a new fitness function in the regulation of the resource use, migration cost, and energy consumption of VM placement. The experimentation of the proposed VM migration strategy is performed over 4 cloud setups with a different configuration which are simulated using CloudSim toolkit.</abstract><cop>Chichester</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/dac.3539</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-8562-3594</orcidid></addata></record> |
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| issn | 1074-5351 1099-1131 |
| language | eng |
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| source | Wiley Online Library All Journals |
| subjects | Cloud computing Computer simulation Computing costs Data centers Energy consumption Experimentation Fitness Genetic algorithms Lion optimization algorithm Optimization algorithms Particle swarm optimization Placement Quality of service architectures Virtual environments virtual machine migration Whale optimization algorithm |
| title | A Lion‐Whale optimization‐based migration of virtual machines for data centers in cloud computing |
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