File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Dynamic optimization of multiattribute resource allocation in self-organizing clouds

TitleDynamic optimization of multiattribute resource allocation in self-organizing clouds
Authors
KeywordsCloud computing
P2P multiattribute range query
VM-multiplexing resource allocation
Convex optimization
Issue Date2013
PublisherIEEE. The Journal's web site is located at http://www.computer.org/tpds
Citation
IEEE Transactions on Parallel and Distributed Systems, 2013, v. 24 n. 3, p. 464-478 How to Cite?
AbstractBy leveraging virtual machine (VM) technology which provides performance and fault isolation, cloud resources can be provisioned on demand in a fine grained, multiplexed manner rather than in monolithic pieces. By integrating volunteer computing into cloud architectures, we envision a gigantic self-organizing cloud (SOC) being formed to reap the huge potential of untapped commodity computing power over the Internet. Toward this new architecture where each participant may autonomously act as both resource consumer and provider, we propose a fully distributed, VM-multiplexing resource allocation scheme to manage decentralized resources. Our approach not only achieves maximized resource utilization using the proportional share model (PSM), but also delivers provably and adaptively optimal execution efficiency. We also design a novel multiattribute range query protocol for locating qualified nodes. Contrary to existing solutions which often generate bulky messages per request, our protocol produces only one lightweight query message per task on the Content Addressable Network (CAN). It works effectively to find for each task its qualified resources under a randomized policy that mitigates the contention among requesters. We show the SOC with our optimized algorithms can make an improvement by 15-60 percent in system throughput than a P2P Grid model. Our solution also exhibits fairly high adaptability in a dynamic node-churning environment.
Persistent Identifierhttp://hdl.handle.net/10722/153180
ISSN
2015 Impact Factor: 2.661
2015 SCImago Journal Rankings: 1.590
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorDi, Sen_US
dc.contributor.authorWang, CLen_US
dc.date.accessioned2012-07-16T09:59:19Z-
dc.date.available2012-07-16T09:59:19Z-
dc.date.issued2013en_US
dc.identifier.citationIEEE Transactions on Parallel and Distributed Systems, 2013, v. 24 n. 3, p. 464-478en_US
dc.identifier.issn1045-9219-
dc.identifier.urihttp://hdl.handle.net/10722/153180-
dc.description.abstractBy leveraging virtual machine (VM) technology which provides performance and fault isolation, cloud resources can be provisioned on demand in a fine grained, multiplexed manner rather than in monolithic pieces. By integrating volunteer computing into cloud architectures, we envision a gigantic self-organizing cloud (SOC) being formed to reap the huge potential of untapped commodity computing power over the Internet. Toward this new architecture where each participant may autonomously act as both resource consumer and provider, we propose a fully distributed, VM-multiplexing resource allocation scheme to manage decentralized resources. Our approach not only achieves maximized resource utilization using the proportional share model (PSM), but also delivers provably and adaptively optimal execution efficiency. We also design a novel multiattribute range query protocol for locating qualified nodes. Contrary to existing solutions which often generate bulky messages per request, our protocol produces only one lightweight query message per task on the Content Addressable Network (CAN). It works effectively to find for each task its qualified resources under a randomized policy that mitigates the contention among requesters. We show the SOC with our optimized algorithms can make an improvement by 15-60 percent in system throughput than a P2P Grid model. Our solution also exhibits fairly high adaptability in a dynamic node-churning environment.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://www.computer.org/tpds-
dc.relation.ispartofIEEE Transactions on Parallel and Distributed Systemsen_US
dc.rightsIEEE Transactions on Parallel and Distributed Systems. Copyright © IEEE.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCloud computing-
dc.subjectP2P multiattribute range query-
dc.subjectVM-multiplexing resource allocation-
dc.subjectConvex optimization-
dc.titleDynamic optimization of multiattribute resource allocation in self-organizing cloudsen_US
dc.typeArticleen_US
dc.identifier.emailDi, S: sdi@cs.hku.hken_US
dc.identifier.emailWang, CL: clwang@cs.hku.hk-
dc.identifier.authorityWang, CL=rp00183en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TPDS.2012.144-
dc.identifier.scopuseid_2-s2.0-84873825749-
dc.identifier.hkuros201833en_US
dc.identifier.hkuros211143-
dc.identifier.volume24-
dc.identifier.issue3-
dc.identifier.spage464-
dc.identifier.epage478-
dc.identifier.isiWOS:000313816300005-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats