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An execution context optimization framework for disk energy
Descriptive
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Title
An execution context optimization framework for disk energy
Name
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Hom
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Jerry Yin
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Jerry Yin Hom
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author
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Kremer
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Ulrich
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Advisory Committee
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Ulrich Kremer
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chair
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Bianchini
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Ricardo
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Advisory Committee
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Ricardo Bianchini
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Martin
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Rich
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Advisory Committee
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Rich Martin
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Bellosa
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Frank
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Advisory Committee
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Frank Bellosa
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outside member
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Rutgers University
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Graduate School - New Brunswick
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theses
OriginInfo
DateCreated
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2008
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2008-05
Language
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English
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electronic
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xiv, 99 pages
Abstract
Power, energy, and thermal concerns have had explosive growth in research over the past two decades. In servers, desktops, and mobile
systems, the hard disk is among the top resources in power and energy consumption. Common techniques for reducing disk energy consumption have included caching, adaptive low power modes, batch scheduling, and data migration. Many previous software optimizations for single disk systems have assumed and experimented in uniprogramming environments.
However, modern systems are typically multiprogramming, and the optimizations do not extend well from the uniprogramming model.
Programs should be aware of concurrently running programs to enable cooperation and coordinate disk accesses from multiple programs. The set of concurrently running programs is referred to as an execution context. Execution context optimizations were introduced to target multiprogramming environments. My research introduces an optimization framework to provide execution context information and reduce disk energy consumption by effectively managing disk accesses.
Optimizing over all possible execution contexts is counter-productive because many contexts do not occur in practice. For an extreme example, users rarely, if at all, run more than twenty programs concurrently. Optimizations may be profitably targeted at the most common execution contexts for a given workload. A study was conducted of real workloads by collecting user activity traces and characterizing the execution contexts. Out of hundreds of contexts
and over 50 unique programs, the study confirmed the intuition that users generally run only a small set of programs at a time.
Execution context optimizations were implemented on eight streaming and interactive applications. The optimizations were compared to previous best optimizations and evaluated on a laptop disk which is already designed for energy efficiency. The disk energy was measured while running synthetic traces of ten execution contexts. The results show up to 63% energy savings while incurring less than 1%
performance delay. When compared to unoptimized versions, energy savings was up to 77%. If the optimizations were applied to
comparable applications in the user study, an estimated 9% disk energy could have been saved. Execution context optimizations show
significant promise for saving disk energy.
systems, the hard disk is among the top resources in power and energy consumption. Common techniques for reducing disk energy consumption have included caching, adaptive low power modes, batch scheduling, and data migration. Many previous software optimizations for single disk systems have assumed and experimented in uniprogramming environments.
However, modern systems are typically multiprogramming, and the optimizations do not extend well from the uniprogramming model.
Programs should be aware of concurrently running programs to enable cooperation and coordinate disk accesses from multiple programs. The set of concurrently running programs is referred to as an execution context. Execution context optimizations were introduced to target multiprogramming environments. My research introduces an optimization framework to provide execution context information and reduce disk energy consumption by effectively managing disk accesses.
Optimizing over all possible execution contexts is counter-productive because many contexts do not occur in practice. For an extreme example, users rarely, if at all, run more than twenty programs concurrently. Optimizations may be profitably targeted at the most common execution contexts for a given workload. A study was conducted of real workloads by collecting user activity traces and characterizing the execution contexts. Out of hundreds of contexts
and over 50 unique programs, the study confirmed the intuition that users generally run only a small set of programs at a time.
Execution context optimizations were implemented on eight streaming and interactive applications. The optimizations were compared to previous best optimizations and evaluated on a laptop disk which is already designed for energy efficiency. The disk energy was measured while running synthetic traces of ten execution contexts. The results show up to 63% energy savings while incurring less than 1%
performance delay. When compared to unoptimized versions, energy savings was up to 77%. If the optimizations were applied to
comparable applications in the user study, an estimated 9% disk energy could have been saved. Execution context optimizations show
significant promise for saving disk energy.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 89-95).
Subject
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Topic
Computer Science
Subject
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Topic
Operating systems (Computer)
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Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17327
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ETD_921
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Identifier
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doi:10.7282/T3DB826T
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ETD doctoral
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Rights
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The author owns the copyright to this work.
Copyright
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Copyright protected
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Open
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Name
Jerry Hom
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Rutgers University. Graduate School - New Brunswick
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Non-exclusive ETD license
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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