Persistent Link:
http://hdl.handle.net/10150/205424
Title:
OPTIMIZING PROCESSOR AND MEMORY FOR GREEN COMPUTING
Author:
Bi, Mingsong
Issue Date:
2011
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Embargo:
Embargo: Release after 12/12/2012
Abstract:
Energy efficiency has become one of the most important factors in the development of computer systems. Increasingly power-hungry processors and memory subsystem have reinforced the need for aggressive power management. Dynamic voltage scaling has become a common consideration for designing energy efficient CPUs in systems ranging from portable devices to large-scale systems. As applications become more data centric and put more pressure on memory subsystem, managing energy consumption of main memory is also becoming critical. Subsequently in this dissertation, we address the issues in designing energy efficient CPU and memory for personal computing devices as well as large-scale systems.For large-scale systems, we address memory subsystem dedicated to buffer cache which accounts for the majority of memory usage in server environment. We take advantage of the I/O handling routines in the OS kernel to hide the delay incurred by the memory state transition so that performance degradation is minimized while high energy savings is achieved. We also address interactive workloads, which account for the bulk of the processing demand on modern mobile or desktop systems. We propose Interaction-Aware Dynamic Voltage Scaling (IADVS) for CPU and Interaction-Aware Memory Energy Management (IAMEM) for memory. The IA framework relies on automatic correlation of user-initiated tasks with the demand placed on CPU and memory to accurately predict power states for CPU and memory. Both mechanisms achieve maximal energy savings while minimizing the impact on the application's performance.
Type:
text; Electronic Dissertation
Keywords:
OS; Processor; Computer Science; Energy; Memory
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Computer Science
Degree Grantor:
University of Arizona
Advisor:
Gniady, Christopher

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleOPTIMIZING PROCESSOR AND MEMORY FOR GREEN COMPUTINGen_US
dc.creatorBi, Mingsongen_US
dc.contributor.authorBi, Mingsongen_US
dc.date.issued2011-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.releaseEmbargo: Release after 12/12/2012en_US
dc.description.abstractEnergy efficiency has become one of the most important factors in the development of computer systems. Increasingly power-hungry processors and memory subsystem have reinforced the need for aggressive power management. Dynamic voltage scaling has become a common consideration for designing energy efficient CPUs in systems ranging from portable devices to large-scale systems. As applications become more data centric and put more pressure on memory subsystem, managing energy consumption of main memory is also becoming critical. Subsequently in this dissertation, we address the issues in designing energy efficient CPU and memory for personal computing devices as well as large-scale systems.For large-scale systems, we address memory subsystem dedicated to buffer cache which accounts for the majority of memory usage in server environment. We take advantage of the I/O handling routines in the OS kernel to hide the delay incurred by the memory state transition so that performance degradation is minimized while high energy savings is achieved. We also address interactive workloads, which account for the bulk of the processing demand on modern mobile or desktop systems. We propose Interaction-Aware Dynamic Voltage Scaling (IADVS) for CPU and Interaction-Aware Memory Energy Management (IAMEM) for memory. The IA framework relies on automatic correlation of user-initiated tasks with the demand placed on CPU and memory to accurately predict power states for CPU and memory. Both mechanisms achieve maximal energy savings while minimizing the impact on the application's performance.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectOSen_US
dc.subjectProcessoren_US
dc.subjectComputer Scienceen_US
dc.subjectEnergyen_US
dc.subjectMemoryen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorGniady, Christopheren_US
dc.contributor.committeememberHartman, Johnen_US
dc.contributor.committeememberLowenthal, Daviden_US
dc.contributor.committeememberZhang, Beichuanen_US
dc.contributor.committeememberGniady, Christopheren_US
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