Show simple item record

dc.contributor.authorBerral García, Josep Lluís
dc.contributor.authorGavaldà Mestre, Ricard
dc.contributor.authorTorres Viñals, Jordi
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics
dc.identifier.citationBerral, J.; Gavaldà, R.; Torres, J. Adaptive scheduling on power-aware managed data-centers using machine learning. A: IEEE/ACM International Conference on Grid Computing. "12th IEEE/ACM International Conference on Grid Computing (GRID 2011)". Lyon: IEEE Computer Society Publications, 2011, p. 66-73.
dc.description.abstractEnergy-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power-aware resource management strategies, also known as “Green IT”. Here we propose an autonomic scheduling of tasks and web-services over cloud environments, focusing on the profit optimization by executing a set of tasks according to servicelevel agreements minus its costs like power consumption. The principal contribution is the use of machine learning techniques in order to predict a priori resource usages, like CPU consumption, and estimate the tasks response time based on the monitored data traffic characteristics. Further, in order to optimize the scheduling, an exact solver based on mixed integer linear programming is used as a proof of concept, and also compared to some approximate algorithm solvers to find valid alternatives for the NP-hard problem of exact schedule solving. Experiments show that machine learning algorithms can predict system behaviors with acceptable accuracy, also the ILP solver obtains the optimal solution managing to adjust appropriately the schedule according to profits and cost of power increases, also reducing migrations when their cost is taken into consideration. Finally, is demonstrated that one of the approximate algorithm solvers is much faster but close in terms of the optimization goal to the exact solver.
dc.format.extent8 p.
dc.publisherIEEE Computer Society Publications
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat
dc.subject.lcshData centers
dc.subject.lcshMachine learning
dc.titleAdaptive scheduling on power-aware managed data-centers using machine learning
dc.typeConference report
dc.subject.lemacAprenentatge automàtic -- Algorismes
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.contributor.groupUniversitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
dc.description.versionPostprint (published version)
upcommons.citation.authorBerral, J.; Gavaldà, R.; Torres, J.
upcommons.citation.contributorIEEE/ACM International Conference on Grid Computing
upcommons.citation.publicationName12th IEEE/ACM International Conference on Grid Computing (GRID 2011)

Files in this item


This item appears in the following Collection(s)

Show simple item record

All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder