Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/2117/7182

Towards energy-aware scheduling in data centers using machine learning
Berral García, Josep Lluís; Goiri Presa, Íñigo; Nou Castell, Ramon; Julià, Ferran; Guitart Fernández, Jordi; Gavaldà Mestre, Ricard; Torres Viñals, Jordi
Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors; Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. CAP - Grup de Computació d´Altes Prestacions; Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge
As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to nd better and more efficient power-aware resource management strategies. There is a growing interest in "Green" IT and there is still a big gap in this area to be covered. In order to obtain an energy-efficient data center, we propose a framework that provides an intelligent consolidation methodology using di erent techniques such as turning on/o ff machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while maximizing performance. For the machine learning approach, we use models learned from previous system behaviors in order to predict power consumption levels, CPU loads, and SLA timings, and improve scheduling decisions. Our framework is vertical, because it considers from watt consumption to workload features, and cross-disciplinary, as it uses a wide variety of techniques. We evaluate these techniques with a framework that covers the whole control cycle of a real scenario, using a simulation with representative heterogeneous workloads, and we measure the quality of the results according to a set of metrics focused toward our goals, besides traditional policies. The results obtained indicate that our approach is close to the optimal placement and behaves better when the level of uncertainty increases.
Peer Reviewed
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
Modeling -- Techniques
Knowledge acquisition (Expert systems)
Coneixement -- Adquisició (Sistemes experts)
Modelatge -- Tècnica
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/conferenceObject
         

Mostrar el registro completo del ítem

Documentos relacionados

Otros documentos del mismo autor/a

Goiri Presa, Íñigo; Berral García, Josep Lluís; Fitó, Josep Oriol; Julià Massó, Ferran; Nou Castell, Ramon; Guitart Fernández, Jordi; Gavaldà Mestre, Ricard; Torres Viñals, Jordi
Goiri Presa, Íñigo; Fitó, Josep Oriol; Julià Masso, Ferran; Nou Castell, Ramon; Berral García, Josep Lluís; Guitart Fernández, Jordi; Torres Viñals, Jordi
Goiri Presa, Íñigo; Julià Massó, Ferran; Nou Castell, Ramon; Berral García, Josep Lluís; Guitart Fernández, Jordi; Torres Viñals, Jordi
Goiri Presa, Íñigo; Julià, Ferran; Ejarque, Jorge; Palol, Marc de; Badia Sala, Rosa Maria; Guitart Fernández, Jordi; Torres Viñals, Jordi
Ejarque, Jorge; Palol, Marc de; Goiri Presa, Íñigo; Julià, Ferran; Guitart Fernández, Jordi; Badia Sala, Rosa Maria; Torres Viñals, Jordi