Cloud computing is an emerging computing paradigm in which "Everything is as a Service", including the provision of virtualized computing infrastructures (known as Infrastructure-as-a-Service modality) hosted on the physical infrastructure, owned by an infrastructure provider. The goal of this infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with its customers and, at the same time, by lowering infrastructure costs among which energy consumption plays a major role. In this paper, we propose a framework able to automatically manage 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.

Exploiting VM Migration for the Automated Power and Performance Management of Green Cloud Computing Systems

GUAZZONE, MARCO;ANGLANO, Cosimo Filomeno;CANONICO, MASSIMO
2012-01-01

Abstract

Cloud computing is an emerging computing paradigm in which "Everything is as a Service", including the provision of virtualized computing infrastructures (known as Infrastructure-as-a-Service modality) hosted on the physical infrastructure, owned by an infrastructure provider. The goal of this infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with its customers and, at the same time, by lowering infrastructure costs among which energy consumption plays a major role. In this paper, we propose a framework able to automatically manage 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
First International Workshop on Energy-Efficient Data Centres (E2DC 2012)
Madrid, Spain
2012
Energy Efficient Data Centers
Springer Berlin Heidelberg
7396
81
92
http://dx.doi.org/10.1007/978-3-642-33645-4_8
Cloud computing; Green computing; SLA
M. Guazzone; C. Anglano; M. Canonico
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/141924
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