Hybrid Clouds brokering: Business opportunities, QoS and energy-saving issues (Articolo in rivista)

Type
Label
  • Hybrid Clouds brokering: Business opportunities, QoS and energy-saving issues (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1016/j.simpat.2013.01.004 (literal)
Alternative label
  • Alfonso Quarati , Andrea Clematis, Antonella Galizia, Daniele D'Agostino (2013)
    Hybrid Clouds brokering: Business opportunities, QoS and energy-saving issues
    in Simulation modelling practice and theory; Elsevier, Paris (Paesi Bassi)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Alfonso Quarati , Andrea Clematis, Antonella Galizia, Daniele D'Agostino (literal)
Pagina inizio
  • 121 (literal)
Pagina fine
  • 134 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Available online 20 February 2013 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 39 (literal)
Rivista
Note
  • ISI Web of Science (WOS) (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Institute of Applied Mathematics and Information Technologies National Research Council, 16149 Genoa, Italy (literal)
Titolo
  • Hybrid Clouds brokering: Business opportunities, QoS and energy-saving issues (literal)
Abstract
  • Hybrid Clouds couple the scalability offered by public Clouds with the greater control supplied by private ones. A (hybrid) Cloud broker acting as an intermediary between users and providers of public Cloud services, may support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. The paper presents a Cloud brokering algorithm delivering services with different level of non-functional requirements, to the private or public resources, on the basis of different scheduling criteria. With the objective of maximize user satisfaction and broker's revenues, the algorithm pursues profit increases by reducing energy costs, through the adoption of energy saving mechanisms. A simulation model is used to evaluate performance in terms of broker's revenue, user satisfaction and energy behavior of various allocation policies. Simulation results show that differences among policies depend on system loads and that the use of turn on and off techniques greatly improves energy savings at low and medium load rates. (literal)
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