TY - GEN
T1 - Exploiting VM migration for the automated power and performance management of green cloud computing systems
AU - Guazzone, Marco
AU - Anglano, Cosimo
AU - Canonico, Massimo
N1 - Funding Information:
This work was supported in part by the Italian Research Ministry under the PRIN 2008 Energy eFFIcient teChnologIEs for the Networks of Tomorrow (EFFICIENT) project.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Cloud computing
KW - Green computing
KW - SLA
UR - http://www.scopus.com/inward/record.url?scp=84867877014&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-33645-4_8
DO - 10.1007/978-3-642-33645-4_8
M3 - Conference contribution
AN - SCOPUS:84867877014
SN - 9783642336447
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 81
EP - 92
BT - Energy Efficient Data Centers - First International Workshop, E2DC 2012, Revised Selected Papers
T2 - 1st International Workshop on Energy Efficient Data Centers, E2DC 2012
Y2 - 8 May 2012 through 8 May 2012
ER -