A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers
Summary One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times sc...
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| Veröffentlicht in: | Software, practice & experience practice & experience, 2019-04, Vol.49 (4), p.617-639 |
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| container_title | Software, practice & experience |
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| creator | Duggan, M. Shaw, R. Duggan, J. Howley, E. Barrett, E. |
| description | Summary
One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center. |
| doi_str_mv | 10.1002/spe.2635 |
| format | Article |
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One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.</description><identifier>ISSN: 0038-0644</identifier><identifier>EISSN: 1097-024X</identifier><identifier>DOI: 10.1002/spe.2635</identifier><language>eng</language><publisher>Bognor Regis: Wiley Subscription Services, Inc</publisher><subject>Algorithms ; Bandwidths ; Cloud computing ; Computer centers ; CPU ; Data centers ; Forecasting ; network bandwidth ; neural network ; prediction algorithms ; Recurrent neural networks</subject><ispartof>Software, practice & experience, 2019-04, Vol.49 (4), p.617-639</ispartof><rights>2018 John Wiley & Sons, Ltd.</rights><rights>2019 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2935-9d255feaa0072117338fd7b669ac9b9d32043679f644a0f0b23358019ce2770d3</citedby><cites>FETCH-LOGICAL-c2935-9d255feaa0072117338fd7b669ac9b9d32043679f644a0f0b23358019ce2770d3</cites><orcidid>0000-0001-9576-3884</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%2Fspe.2635$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fspe.2635$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids></links><search><creatorcontrib>Duggan, M.</creatorcontrib><creatorcontrib>Shaw, R.</creatorcontrib><creatorcontrib>Duggan, J.</creatorcontrib><creatorcontrib>Howley, E.</creatorcontrib><creatorcontrib>Barrett, E.</creatorcontrib><title>A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers</title><title>Software, practice & experience</title><description>Summary
One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.</description><subject>Algorithms</subject><subject>Bandwidths</subject><subject>Cloud computing</subject><subject>Computer centers</subject><subject>CPU</subject><subject>Data centers</subject><subject>Forecasting</subject><subject>network bandwidth</subject><subject>neural network</subject><subject>prediction algorithms</subject><subject>Recurrent neural networks</subject><issn>0038-0644</issn><issn>1097-024X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp10MtKAzEUBuAgCtYq-AgBN26mniQzk8mylHqBgoIK7kKaZNqUuZnMKN35CD6jT2LaunX1bz7O5UfoksCEANCb0NkJzVl2hEYEBE-Apm_HaATAigTyND1FZyFsAAjJaD5Ceorroepd72r78_UdetuFmGptlcGdt8bp3rUNVl3nW6XXuGw9DnptzVC5ZoUr92Fx7VZe7ZlrsK7awWCjeoW1bXrrwzk6KVUV7MVfjtHr7fxldp8sHu8eZtNFoqlgWSIMzbLSKgXAKSGcsaI0fJnnQmmxFIZRSFnORRm_UFDCkjKWFUCEtpRzMGyMrg5z46nvgw293LSDb-JKSUnBGae5oFFdH5T2bQjelrLzrlZ-KwnIXYUyVih3FUaaHOinq-z2Xyefn-Z7_wsAm3RH</recordid><startdate>201904</startdate><enddate>201904</enddate><creator>Duggan, M.</creator><creator>Shaw, R.</creator><creator>Duggan, J.</creator><creator>Howley, E.</creator><creator>Barrett, E.</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-9576-3884</orcidid></search><sort><creationdate>201904</creationdate><title>A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers</title><author>Duggan, M. ; Shaw, R. ; Duggan, J. ; Howley, E. ; Barrett, E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2935-9d255feaa0072117338fd7b669ac9b9d32043679f644a0f0b23358019ce2770d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Bandwidths</topic><topic>Cloud computing</topic><topic>Computer centers</topic><topic>CPU</topic><topic>Data centers</topic><topic>Forecasting</topic><topic>network bandwidth</topic><topic>neural network</topic><topic>prediction algorithms</topic><topic>Recurrent neural networks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Duggan, M.</creatorcontrib><creatorcontrib>Shaw, R.</creatorcontrib><creatorcontrib>Duggan, J.</creatorcontrib><creatorcontrib>Howley, E.</creatorcontrib><creatorcontrib>Barrett, E.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Software, practice & experience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Duggan, M.</au><au>Shaw, R.</au><au>Duggan, J.</au><au>Howley, E.</au><au>Barrett, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers</atitle><jtitle>Software, practice & experience</jtitle><date>2019-04</date><risdate>2019</risdate><volume>49</volume><issue>4</issue><spage>617</spage><epage>639</epage><pages>617-639</pages><issn>0038-0644</issn><eissn>1097-024X</eissn><abstract>Summary
One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime‐ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.</abstract><cop>Bognor Regis</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/spe.2635</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-9576-3884</orcidid></addata></record> |
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| ispartof | Software, practice & experience, 2019-04, Vol.49 (4), p.617-639 |
| issn | 0038-0644 1097-024X |
| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Bandwidths Cloud computing Computer centers CPU Data centers Forecasting network bandwidth neural network prediction algorithms Recurrent neural networks |
| title | A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers |
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