A Case Study on Job Scheduling Policy for Workload Characterization and Power Efficiency
With the increasing popularity of cloud computing, datacenters are becoming more important than ever before. A typical datacenter typically consists of a large number of homogeneous or heterogeneous servers connected by networks. Unfortunately, these servers and network equipment are often under-uti...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2014-05 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Aftab Ahmed Chandio Yu, Zhibin Feroz Shah Syed Imtiaz Ali Korejo |
| description | With the increasing popularity of cloud computing, datacenters are becoming more important than ever before. A typical datacenter typically consists of a large number of homogeneous or heterogeneous servers connected by networks. Unfortunately, these servers and network equipment are often under-utilized and power hungry. To improve the utilization of hardware resources and make them power efficiency in datacenters, workload characterization and analysis is at the foundation. In this paper, we characterize and analyze the job arriving rate, arriving time, job length, power consumption, and temperature dissipation in a real world datacenter by using statistical methods. From the characterization, we find unique features in the workload can be used to optimize the resource utilization and power consumption of datacenters |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2084123173</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2084123173</sourcerecordid><originalsourceid>FETCH-proquest_journals_20841231733</originalsourceid><addsrcrecordid>eNqNi08LgjAcQEcQJOV3-EFnQTdNryFGdAoK6iZrf3I2ttomYZ8-D32ATu_w3puhCBOSJVWO8QLF3vdpmuJNiYuCROi6hZp6Aacw8BGsgYO9wYl1gg9amTscrVZsBGkdXKx7aEs51B11lAXh1IcGNT3U8Cl8CweNlIopYdi4QnNJtRfxj0u03jXnep88nX0Nwoe2t4Mzk2pxWuUZJllJyH_VFxZGQSk</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2084123173</pqid></control><display><type>article</type><title>A Case Study on Job Scheduling Policy for Workload Characterization and Power Efficiency</title><source>Free E- Journals</source><creator>Aftab Ahmed Chandio ; Yu, Zhibin ; Feroz Shah Syed ; Imtiaz Ali Korejo</creator><creatorcontrib>Aftab Ahmed Chandio ; Yu, Zhibin ; Feroz Shah Syed ; Imtiaz Ali Korejo</creatorcontrib><description>With the increasing popularity of cloud computing, datacenters are becoming more important than ever before. A typical datacenter typically consists of a large number of homogeneous or heterogeneous servers connected by networks. Unfortunately, these servers and network equipment are often under-utilized and power hungry. To improve the utilization of hardware resources and make them power efficiency in datacenters, workload characterization and analysis is at the foundation. In this paper, we characterize and analyze the job arriving rate, arriving time, job length, power consumption, and temperature dissipation in a real world datacenter by using statistical methods. From the characterization, we find unique features in the workload can be used to optimize the resource utilization and power consumption of datacenters</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Cloud computing ; Data centers ; Power consumption ; Power efficiency ; Servers ; Statistical methods ; Workload ; Workloads</subject><ispartof>arXiv.org, 2014-05</ispartof><rights>2014. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>780,784</link.rule.ids></links><search><creatorcontrib>Aftab Ahmed Chandio</creatorcontrib><creatorcontrib>Yu, Zhibin</creatorcontrib><creatorcontrib>Feroz Shah Syed</creatorcontrib><creatorcontrib>Imtiaz Ali Korejo</creatorcontrib><title>A Case Study on Job Scheduling Policy for Workload Characterization and Power Efficiency</title><title>arXiv.org</title><description>With the increasing popularity of cloud computing, datacenters are becoming more important than ever before. A typical datacenter typically consists of a large number of homogeneous or heterogeneous servers connected by networks. Unfortunately, these servers and network equipment are often under-utilized and power hungry. To improve the utilization of hardware resources and make them power efficiency in datacenters, workload characterization and analysis is at the foundation. In this paper, we characterize and analyze the job arriving rate, arriving time, job length, power consumption, and temperature dissipation in a real world datacenter by using statistical methods. From the characterization, we find unique features in the workload can be used to optimize the resource utilization and power consumption of datacenters</description><subject>Cloud computing</subject><subject>Data centers</subject><subject>Power consumption</subject><subject>Power efficiency</subject><subject>Servers</subject><subject>Statistical methods</subject><subject>Workload</subject><subject>Workloads</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNqNi08LgjAcQEcQJOV3-EFnQTdNryFGdAoK6iZrf3I2ttomYZ8-D32ATu_w3puhCBOSJVWO8QLF3vdpmuJNiYuCROi6hZp6Aacw8BGsgYO9wYl1gg9amTscrVZsBGkdXKx7aEs51B11lAXh1IcGNT3U8Cl8CweNlIopYdi4QnNJtRfxj0u03jXnep88nX0Nwoe2t4Mzk2pxWuUZJllJyH_VFxZGQSk</recordid><startdate>20140514</startdate><enddate>20140514</enddate><creator>Aftab Ahmed Chandio</creator><creator>Yu, Zhibin</creator><creator>Feroz Shah Syed</creator><creator>Imtiaz Ali Korejo</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20140514</creationdate><title>A Case Study on Job Scheduling Policy for Workload Characterization and Power Efficiency</title><author>Aftab Ahmed Chandio ; Yu, Zhibin ; Feroz Shah Syed ; Imtiaz Ali Korejo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_20841231733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Cloud computing</topic><topic>Data centers</topic><topic>Power consumption</topic><topic>Power efficiency</topic><topic>Servers</topic><topic>Statistical methods</topic><topic>Workload</topic><topic>Workloads</topic><toplevel>online_resources</toplevel><creatorcontrib>Aftab Ahmed Chandio</creatorcontrib><creatorcontrib>Yu, Zhibin</creatorcontrib><creatorcontrib>Feroz Shah Syed</creatorcontrib><creatorcontrib>Imtiaz Ali Korejo</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Aftab Ahmed Chandio</au><au>Yu, Zhibin</au><au>Feroz Shah Syed</au><au>Imtiaz Ali Korejo</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>A Case Study on Job Scheduling Policy for Workload Characterization and Power Efficiency</atitle><jtitle>arXiv.org</jtitle><date>2014-05-14</date><risdate>2014</risdate><eissn>2331-8422</eissn><abstract>With the increasing popularity of cloud computing, datacenters are becoming more important than ever before. A typical datacenter typically consists of a large number of homogeneous or heterogeneous servers connected by networks. Unfortunately, these servers and network equipment are often under-utilized and power hungry. To improve the utilization of hardware resources and make them power efficiency in datacenters, workload characterization and analysis is at the foundation. In this paper, we characterize and analyze the job arriving rate, arriving time, job length, power consumption, and temperature dissipation in a real world datacenter by using statistical methods. From the characterization, we find unique features in the workload can be used to optimize the resource utilization and power consumption of datacenters</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2014-05 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_2084123173 |
| source | Free E- Journals |
| subjects | Cloud computing Data centers Power consumption Power efficiency Servers Statistical methods Workload Workloads |
| title | A Case Study on Job Scheduling Policy for Workload Characterization and Power Efficiency |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-29T03%3A17%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=A%20Case%20Study%20on%20Job%20Scheduling%20Policy%20for%20Workload%20Characterization%20and%20Power%20Efficiency&rft.jtitle=arXiv.org&rft.au=Aftab%20Ahmed%20Chandio&rft.date=2014-05-14&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E2084123173%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2084123173&rft_id=info:pmid/&rfr_iscdi=true |