Algorithms for power savings
This article examines two different mechanisms for saving power in battery-operated embedded systems. The first strategy is that the system can be placed in a sleep state if it is idle. However, a fixed amount of energy is required to bring the system back into an active state in which it can resume...
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Veröffentlicht in: | ACM transactions on algorithms 2007-11, Vol.3 (4), p.41 |
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description | This article examines two different mechanisms for saving power in battery-operated embedded systems. The first strategy is that the system can be placed in a sleep state if it is idle. However, a fixed amount of energy is required to bring the system back into an active state in which it can resume work. The second way in which power savings can be achieved is by varying the speed at which jobs are run. We utilize a power consumption curve
P
(
s
) which indicates the power consumption level given a particular speed. We assume that
P
(
s
) is convex, nondecreasing, and nonnegative for
s
≥ 0. The problem is to schedule arriving jobs in a way that minimizes total energy use and so that each job is completed after its release time and before its deadline. We assume that all jobs can be preempted and resumed at no cost. Although each problem has been considered separately, this is the first theoretical analysis of systems that can use both mechanisms. We give an offline algorithm that is within a factor of 2 of the optimal algorithm. We also give an online algorithm with a constant competitive ratio. |
doi_str_mv | 10.1145/1290672.1290678 |
format | Article |
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P
(
s
) which indicates the power consumption level given a particular speed. We assume that
P
(
s
) is convex, nondecreasing, and nonnegative for
s
≥ 0. The problem is to schedule arriving jobs in a way that minimizes total energy use and so that each job is completed after its release time and before its deadline. We assume that all jobs can be preempted and resumed at no cost. Although each problem has been considered separately, this is the first theoretical analysis of systems that can use both mechanisms. We give an offline algorithm that is within a factor of 2 of the optimal algorithm. We also give an online algorithm with a constant competitive ratio.</description><identifier>ISSN: 1549-6325</identifier><identifier>EISSN: 1549-6333</identifier><identifier>DOI: 10.1145/1290672.1290678</identifier><language>eng</language><ispartof>ACM transactions on algorithms, 2007-11, Vol.3 (4), p.41</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c272t-b6a9560a358ea5d8759b42d2a2c1cd35f8f763fe48d30bb86fb6f489aad330023</citedby><cites>FETCH-LOGICAL-c272t-b6a9560a358ea5d8759b42d2a2c1cd35f8f763fe48d30bb86fb6f489aad330023</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Irani, Sandy</creatorcontrib><creatorcontrib>Shukla, Sandeep</creatorcontrib><creatorcontrib>Gupta, Rajesh</creatorcontrib><title>Algorithms for power savings</title><title>ACM transactions on algorithms</title><description>This article examines two different mechanisms for saving power in battery-operated embedded systems. The first strategy is that the system can be placed in a sleep state if it is idle. However, a fixed amount of energy is required to bring the system back into an active state in which it can resume work. The second way in which power savings can be achieved is by varying the speed at which jobs are run. We utilize a power consumption curve
P
(
s
) which indicates the power consumption level given a particular speed. We assume that
P
(
s
) is convex, nondecreasing, and nonnegative for
s
≥ 0. The problem is to schedule arriving jobs in a way that minimizes total energy use and so that each job is completed after its release time and before its deadline. We assume that all jobs can be preempted and resumed at no cost. Although each problem has been considered separately, this is the first theoretical analysis of systems that can use both mechanisms. We give an offline algorithm that is within a factor of 2 of the optimal algorithm. We also give an online algorithm with a constant competitive ratio.</description><issn>1549-6325</issn><issn>1549-6333</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><recordid>eNo9kDtPwzAURi0EEqUwszBkYktr-_o5VhUvqRILzJaT2CUoqYNvCuLfA0rEdL7h6BsOIdeMrhgTcs24pUrz1URzQhZMClsqADj931yekwvEd0rBApgFudl0-5Tb8a3HIqZcDOkr5AL9Z3vY4yU5i77DcDVzSV7v7162j-Xu-eFpu9mVNdd8LCvlrVTUgzTBy8ZoaSvBG-55zeoGZDRRK4hBmAZoVRkVKxWFsd43AJRyWJLb6XfI6eMYcHR9i3XoOn8I6YgOhLRaaP0rriexzgkxh-iG3PY-fztG3V8FN1eYaeAH91pNzw</recordid><startdate>200711</startdate><enddate>200711</enddate><creator>Irani, Sandy</creator><creator>Shukla, Sandeep</creator><creator>Gupta, Rajesh</creator><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>200711</creationdate><title>Algorithms for power savings</title><author>Irani, Sandy ; Shukla, Sandeep ; Gupta, Rajesh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c272t-b6a9560a358ea5d8759b42d2a2c1cd35f8f763fe48d30bb86fb6f489aad330023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Irani, Sandy</creatorcontrib><creatorcontrib>Shukla, Sandeep</creatorcontrib><creatorcontrib>Gupta, Rajesh</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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>ACM transactions on algorithms</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Irani, Sandy</au><au>Shukla, Sandeep</au><au>Gupta, Rajesh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Algorithms for power savings</atitle><jtitle>ACM transactions on algorithms</jtitle><date>2007-11</date><risdate>2007</risdate><volume>3</volume><issue>4</issue><spage>41</spage><pages>41-</pages><issn>1549-6325</issn><eissn>1549-6333</eissn><abstract>This article examines two different mechanisms for saving power in battery-operated embedded systems. The first strategy is that the system can be placed in a sleep state if it is idle. However, a fixed amount of energy is required to bring the system back into an active state in which it can resume work. The second way in which power savings can be achieved is by varying the speed at which jobs are run. We utilize a power consumption curve
P
(
s
) which indicates the power consumption level given a particular speed. We assume that
P
(
s
) is convex, nondecreasing, and nonnegative for
s
≥ 0. The problem is to schedule arriving jobs in a way that minimizes total energy use and so that each job is completed after its release time and before its deadline. We assume that all jobs can be preempted and resumed at no cost. Although each problem has been considered separately, this is the first theoretical analysis of systems that can use both mechanisms. We give an offline algorithm that is within a factor of 2 of the optimal algorithm. We also give an online algorithm with a constant competitive ratio.</abstract><doi>10.1145/1290672.1290678</doi></addata></record> |
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title | Algorithms for power savings |
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