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Adaptive scheduling on power-aware managed data-centers using machine learning
dc.contributor.author | Berral García, Josep Lluís |
dc.contributor.author | Gavaldà Mestre, Ricard |
dc.contributor.author | Torres Viñals, Jordi |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics |
dc.date.accessioned | 2011-10-05T12:11:25Z |
dc.date.available | 2011-10-05T12:11:25Z |
dc.date.created | 2011 |
dc.date.issued | 2011 |
dc.identifier.citation | Berral, 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.identifier.isbn | 978-0-7695-4572-6 |
dc.identifier.uri | http://hdl.handle.net/2117/13431 |
dc.description.abstract | Energy-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.extent | 8 p. |
dc.language.iso | eng |
dc.publisher | IEEE Computer Society Publications |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat |
dc.subject.lcsh | Data centers |
dc.subject.lcsh | Heuristics |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Scheduling |
dc.title | Adaptive scheduling on power-aware managed data-centers using machine learning |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic -- Algorismes |
dc.subject.lemac | Heurística |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.identifier.doi | 10.1109/Grid.2011.18 |
dc.description.peerreviewed | Peer Reviewed |
dc.rights.access | Restricted access - publisher's policy |
local.identifier.drac | 5984384 |
dc.description.version | Postprint (published version) |
local.citation.author | Berral, J.; Gavaldà, R.; Torres, J. |
local.citation.contributor | IEEE/ACM International Conference on Grid Computing |
local.citation.pubplace | Lyon |
local.citation.publicationName | 12th IEEE/ACM International Conference on Grid Computing (GRID 2011) |
local.citation.startingPage | 66 |
local.citation.endingPage | 73 |