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Impact of GC Design on Power and Performance for Android

Hussein, Ahmed; Payer, Mathias; Hosking, Antony; Vick, Christopher A.

Description

Small mobile devices have evolved to versatile computing systems. Android devices run a complete software stack, including a full Linux kernel, user land with several software daemons and a virtual machine to run applications. On these mobile systems energy is a scarce resource and needs to be handled carefully. Current systems rely on governors that adjust the frequency of individual cores depending on the system load. We measure energy consumption of different components of this complex...[Show more]

dc.contributor.authorHussein, Ahmed
dc.contributor.authorPayer, Mathias
dc.contributor.authorHosking, Antony
dc.contributor.authorVick, Christopher A.
dc.coverage.spatialHaifa, Israel
dc.date.accessioned2016-06-14T23:21:04Z
dc.date.createdMay 26-28, 2015
dc.identifier.isbn9781450336079
dc.identifier.urihttp://hdl.handle.net/1885/103695
dc.description.abstractSmall mobile devices have evolved to versatile computing systems. Android devices run a complete software stack, including a full Linux kernel, user land with several software daemons and a virtual machine to run applications. On these mobile systems energy is a scarce resource and needs to be handled carefully. Current systems rely on governors that adjust the frequency of individual cores depending on the system load. We measure energy consumption of different components of this complex software stack, including garbage collection (GC) of the Android virtual machine. Here we propose several extensions to the default GC configuration of Android, including a generational collector, across established dimensions of heap memory size and concurrency. Our evaluation shows that Android's asynchronous GC thread consumes a significant amount of energy. Therefore, varying the GC strategy can reduce total on-chip energy (by 20--30%) whilst slightly impacting application throughput (by 10--40%) and increasing worst-case pause times (by 20--30%). Our work quantifies the direct impact of GC on mobile system, enumerates the main factors and layers of this relationship, and offers a guide for analysis of memory behavior in understanding and tuning system performance
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.ispartofseriesSYSTOR 2015 The 8th ACM International Systems and Storage Conference
dc.sourceProceedings of the 8th ACM International Systems and Storage Conference, SYSTOR 2015
dc.titleImpact of GC Design on Power and Performance for Android
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2015
local.identifier.absfor080502 - Mobile Technologies
local.identifier.ariespublicationu4056230xPUB521
local.type.statusPublished Version
local.contributor.affiliationHussein, Ahmed, Purdue University
local.contributor.affiliationPayer, Mathias, Purdue University
local.contributor.affiliationHosking, Antony, College of Engineering and Computer Science, ANU
local.contributor.affiliationVick, Christopher A., Purdue University
local.description.embargo2037-12-31
local.identifier.doi10.1145/2757667.2757674
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
dc.date.updated2016-06-14T08:58:28Z
CollectionsANU Research Publications

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