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Power-aware multi-data center management 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 de Llenguatges i Sistemes Informàtics |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.date.accessioned | 2014-06-16T11:09:15Z |
dc.date.available | 2014-06-16T11:09:15Z |
dc.date.created | 2013 |
dc.date.issued | 2013 |
dc.identifier.citation | Berral, J.; Gavaldà, R.; Torres, J. Power-aware multi-data center management using machine learning. A: International Workshop on Power-aware Algorithms, Systems, and Architectures. "International Conference on Parallel Processing: The 42nd Annual Conference, ICPP 2013: 1-4 October 2013 Lyon, France: proceedings". Lyon: 2013, p. 858-867. |
dc.identifier.uri | http://hdl.handle.net/2117/23228 |
dc.description.abstract | The cloud relies upon multi-datacenter (multi-DC) infrastructures distributed along the world, where people and enterprises pay for resources to offer their web-services to worldwide clients. Intelligent management is required to automate and manage these infrastructures, as the amount of resources and data to manage exceeds the capacities of human operators. Also, it must take into account the cost of running the resources (energy) and the quality of service towards web-services and clients. (De-)consolidation and priming proximity to clients become two main strategies to allocate resources and properly place these web-services in the multi-DC network. Here we present a mathematical model to describe the scheduling problem given web-services and hosts across a multi-DC system, enhancing the decision makers with models for the system behavior obtained using machine learning. After running the system on real DC infrastructures we see that the model drives web-services to the best locations given quality of service, energy consumption, and client proximity, also (de-)consolidating according to the resources required for each web-service given its load. |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
dc.subject.lcsh | Cloud computing |
dc.subject.lcsh | Machine learning |
dc.title | Power-aware multi-data center management using machine learning |
dc.type | Conference report |
dc.subject.lemac | Computació en núvol |
dc.subject.lemac | Aprenentatge automàtic |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.identifier.doi | 10.1109/ICPP.2013.102 |
dc.rights.access | Open Access |
local.identifier.drac | 12795820 |
dc.description.version | Postprint (author’s final draft) |
local.citation.author | Berral, J.; Gavaldà, R.; Torres, J. |
local.citation.contributor | International Workshop on Power-aware Algorithms, Systems, and Architectures |
local.citation.pubplace | Lyon |
local.citation.publicationName | International Conference on Parallel Processing: The 42nd Annual Conference, ICPP 2013: 1-4 October 2013 Lyon, France: proceedings |
local.citation.startingPage | 858 |
local.citation.endingPage | 867 |