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Towards a Performance Interference-aware Virtual Machine Placement Strategy for Supporting Soft Real-time Applications in the Cloud

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)es
dc.contributor.authorCaglar, Faruk
dc.contributor.authorShekhar, Shashank
dc.contributor.authorGokhale, Aniruddha
dc.contributor.editorCucinotta, Tommaso
dc.contributor.editorPautet, Laurent
dc.contributor.editorGarcía Valls, Marisol
dc.date.accessioned2014-11-13T11:10:48Z
dc.date.available2014-11-13T11:10:48Z
dc.date.issued2011-11
dc.descriptionREACTION 2014. 3rd International Workshop on Real-time and Distributed Computing in Emerging Applications. Rome, Italy. December 2nd, 2014.es
dc.description.abstractIt is standard practice for cloud service providers (CSPs) to overbook physical system resources to maximize the resource utilization and make their business model more profitable. Resource overbooking can lead to performance interference, however, among the virtual machines (VMs) hosted on the physical resources causing performance un-predictability for soft real-time applications hosted in the VMs, which is unacceptable to these applications. Balancing these conflicting requirements needs a careful design of the placement strategies for hosting soft real-time applications such that the performance interference effects are minimized while still allowing resource overbooking. These placement decisions cannot be made offline because workloads change at run time. Moreover, satisfying the priorities of collocated VMs may require VM migrations, which require an online solution. This paper presents a machine learning-based, online placement solution to this problem where the system is trained using a publicly available trace of a large data center owned by Google. Our approach first classifies the VMs based on their historic mean CPU and memory usage, and performance features. Subsequently, it learns the best patterns of collocating the classified VMs by employing machine learning techniques. These extracted patterns are those that provide the lowest performance interference level on the specified host machines making them amenable to hosting soft real-time applications while still allowing resource overbooking.es
dc.description.sponsorshipThis work was supported in part by the National Science Foundation CAREER CNS 0845789 and AFOSR DDDAS FA9550-13-1-0227.es
dc.description.statusPublicadoes
dc.format.extent6es
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationGarcía Valls, M. et al. (eds.) (2014). 3rd IEEE International Workshop on Real-time and distributed computing in emerging applications. (Co-located with 35th IEEE RTSS). Rome, Italy. December 2nd, 2014. Universidad Carlos III de Madrid, 15-20.es
dc.identifier.isbn978-84-697-1736-3
dc.identifier.publicationfirstpage15es
dc.identifier.publicationlastpage20es
dc.identifier.publicationtitleInternational Workshop on Real-Time and Distributed Computing in Emerging Applications (REACTION 2014)es
dc.identifier.urihttps://hdl.handle.net/10016/19687
dc.languageeng
dc.language.isoenges
dc.publisherUniversidad Carlos III de Madrides
dc.relation.eventdateDecember 2nd, 2014es
dc.relation.eventnumber3
dc.relation.eventplaceRome, Italyes
dc.relation.eventtitleInternational Workshop on Real-Time and Distributed Computing in Emerging Applications (REACTION)es
dc.rights© Copyright by the Authorses
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherSistemas en tiempo reales
dc.subject.otherSistemas distribuidoses
dc.subject.othercloud computinges
dc.subject.otherdistributed systemses
dc.subject.otherReal-time computinges
dc.subject.otherVirtual machine placementes
dc.subject.otherPerformance interferencees
dc.subject.otherResource overbookinges
dc.subject.otherApplication QoSes
dc.titleTowards a Performance Interference-aware Virtual Machine Placement Strategy for Supporting Soft Real-time Applications in the Cloudes
dc.typeconference proceedings*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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REACTION 2014. Third IEEE International Workshop on Real-time and distributed computing in emerging applications