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ReserarchID
CSTBH7J9M
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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This paper attempted to assess the attitudes of students in
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The increasing rate of the demand for computational resources has led to the production of largescale data centers. They consume huge amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Power-related costs have become one of the major economic factors in IT data-centers, and companies and the research community are currently working on new efficient power aware resource management strategies, also known as “Green IT”. Here we propose a framework for autonomic scheduling of tasks based upon some parametric constraints. In this paper we propose an analysis of the critical factors affecting the energy consumption of cloud servers in cloud computing and consideration to make performance very fast by using Sigar API to solve speed problems. In PCBPAS we impose some parametric constraints during task allocation to the server that can be adjusted dynamically to balance the server’s workloads in an efficient way so that CPU consumption can be improved and energy saving be achieved.
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