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dc.contributor.authorEl hadj Khalaf, Abderrahmane-
dc.contributor.authorDjemai, Zakarya-
dc.date.accessioned2020-12-21T12:05:51Z-
dc.date.available2020-12-21T12:05:51Z-
dc.date.issued2020-
dc.identifier.urihttp://di.univ-blida.dz:8080/jspui/handle/123456789/7962-
dc.descriptionill., Bibliogr.fr_FR
dc.description.abstractCloud computing is getting more popular day by day. Nowadays, we have an energy shortage worldwide. Thus, more researches concern about finding ways to improve the energy efficiency and sustainability of cloud computing. This thesis presents a performance analysis of the cloud computing system based on the energy-efficient task scheduling strategy. This strategy consists of waking up threshold policy and a sleep delay timer. To model the system in an energy-efficient task scheduling strategy, we use a vacation queue system with an N-policy threshold. in this work, the system considered as a physical machine with a limited buffer, this physical machine contains a set of virtual machines that represent the servers in our modelization, as well we constructed a continuous-time Markov chain and an infinitesimal generator that allows us to calculate stationary distribution vector. After that, we analyze the steady-state system to calculate the system's different performance measures like energy consumption and the delay of the system. We tested our system with different scenarios and with different parameter values to discover the proposed strategy performance and validate the system model according to the performance measure. The experimental study has proven that the proposed solution improves energy efficiency and latency without affecting availability in the system Key words: Cloud Computing, energy efficiency, task scheduling, waking up threshold sleep delay, queue system, Markov chainfr_FR
dc.language.isofrfr_FR
dc.publisherUniversité Blida 1fr_FR
dc.subjectCloud Computingfr_FR
dc.subjectenergy efficiencyfr_FR
dc.subjecttask schedulingfr_FR
dc.subjectwaking up thresholdfr_FR
dc.subjectsleep delayfr_FR
dc.subjectqueue systemfr_FR
dc.subjectMarkov chainfr_FR
dc.titleAn energy saving model for Cloud Computingfr_FR
dc.typeThesisfr_FR
Collection(s) :Mémoires de Master

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