An Automated Code Optimizer of Design Patterns for Reducing Energy Usage in Green Computing
Keywords:cloud data centre, energy efficiency, DVFS, cloud applications.
Cloud data center contains various resources which are utilized continuously without any break. The data center consumed large energy to keep the components to run 24/7 for achieving certain performances. Such energy usage becomes one of the major issues in the Cloud data center. It is due to the Cloud availability is highly demanded in network community. In this study, we formed an analysis on the energy consumption by considering the CPU usages through the existing Dynamic Voltage and Frequency Scaling (DVFS) techniques that proposed in existing researches. The aim of this work is to investigate and identify Cloud services application factors that can be employed on the DVFS for better energy management in Cloud data center. Our study expresses the areas of the Cloud service applications such as Business Application-as-a-Services, Consumer Application-as-a-Services and Scientific Application as-a Service that embedded in DVFS where it can be part of the energy saving contributors. We also analysed the two main factors in DVFS are proportional to time and frequency that can be adjusted towards energy efficient in the data center. The survey is also investigated on how far such functions can be manipulated for energy saving while investigating potential solutions for further enhancement.
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