A distributed optimization method for the geographically distributed data centres problem

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dc.contributor.authorWahbi, Mohamed
dc.contributor.authorGrimes, Diarmuid
dc.contributor.authorMehta, Deepak
dc.contributor.authorBrown, Kenneth N.
dc.contributor.authorO'Sullivan, Barry
dc.contributor.editorSalvagnin, Domenico
dc.contributor.editorLombardi, Michele
dc.contributor.funderSeventh Framework Programmeen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2017-08-25T11:52:05Z
dc.date.available2017-08-25T11:52:05Z
dc.date.issued2017-06
dc.date.updated2017-08-25T11:23:15Z
dc.description.abstractThe geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method.en
dc.description.sponsorshipScience Foundation Ireland (Grant Number SFI/12/RC/2289)en
dc.description.statusPeer revieweden
dc.description.urihttps://cpaior2017.dei.unipd.it/en
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationWahbi M., Grimes D., Mehta D., Brown K. N., O’Sullivan B. (2017) 'A distributed optimization method for the geographically distributed data centres problem’, in Salvagnin D. and Lombardi M. (eds) Integration of AI and OR Techniques in Constraint Programming. Proceedings of Fourteenth International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, Padua, Italy, 5-8 June. Lecture Notes in Computer Science, 10335, pp. 147-166. doi:10.1007/978-3-319-59776-8_12en
dc.identifier.doi10.1007/978-3-319-59776-8_12
dc.identifier.endpage166en
dc.identifier.issn0302-9743
dc.identifier.journaltitleLecture Notes in Computer Scienceen
dc.identifier.startpage147en
dc.identifier.urihttp://hdl.handle.net/10468/4548
dc.identifier.volume10335en
dc.language.isoenen
dc.publisherSpringer International Publishing AGen
dc.relation.ispartofCPAIOR 2017: Fourteenth International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP1::ICT/608826/EU/Globally optimized ENergy efficient data Centres - GENiC/GENICen
dc.rights© 2017, Springer International Publishing AG. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-59776-8_12en
dc.subjectData centreen
dc.subjectVirtual machineen
dc.subjectDistributed optimization methoden
dc.subjectDistributed constraint optimization frameworken
dc.subjectDCOPen
dc.subjectSemi-asynchronous distributed algorithmen
dc.titleA distributed optimization method for the geographically distributed data centres problemen
dc.typeConference itemen
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