Cloud computing is an emerging computing paradigm which is gaining popularity in IT industry for its appealing property of considering "Everything as a Service". The goal of a cloud infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with service providers, and, at the same time, by lowering infrastructure costs. Among these costs, the energy consumption induced by the cloud infrastructure, for running cloud services, plays a primary role. Unfortunately, the minimization of QoS viola- tions and, at the same time, the reduction of energy consumption is a conflicting and challenging problem. In this thesis, we propose a framework to automatically manage computing resources of cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.

Power and Performance Management in Cloud Computing Systems

GUAZZONE, MARCO
2012-01-01

Abstract

Cloud computing is an emerging computing paradigm which is gaining popularity in IT industry for its appealing property of considering "Everything as a Service". The goal of a cloud infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with service providers, and, at the same time, by lowering infrastructure costs. Among these costs, the energy consumption induced by the cloud infrastructure, for running cloud services, plays a primary role. Unfortunately, the minimization of QoS viola- tions and, at the same time, the reduction of energy consumption is a conflicting and challenging problem. In this thesis, we propose a framework to automatically manage computing resources of cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.
2012
http://dott-informatica.campusnet.unito.it/html/theses/guazzone.pdf
http://people.unipmn.it/sguazt/pubs/Guazzone-2012-Thesis.pdf
Cloud Computing; Control Theory; System Identification; Service Level Agreement; Resource Management; Optimization; Green Computing; energy efficiency
Marco Guazzone
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/141162
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact