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ISSN 1004-9037
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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      30 Dec 2022, Volume 37 Issue 5   
    Article

    A GREEN CLOUD COMPUTING MODEL FOR ENERGY-AWARE MACHINE ALLOCATIONS AND PLACEMENTS
    Nanashheb Bhausaheb Kadu1, Dr. Pramod Pandurang Jadhav1 and Dr. Santosh Pawar2
    Journal of Data Acquisition and Processing, 2022, 37 (5): 1642-1655 . 

    Abstract

    The demand for distributed computing administration has increased due to the cloud's high degree of change and adaptability. To address this, the electrical energy efficiency of cloud server farms needs to be improved more. This proposal suggests and assesses an energy-efficient half-and-half (EEH) plan to improve server farms ability to use power efficiently. Bidding reservation and staff makeup will be considered together instead of using a single technique like in analogous works that have already been proposed. The EEH structure arranges customer requests according to time and resource requirements before bookings are made. The planning decisions use accounting calculations that account for electricity use. Additionally, it contains calculations performed by the union to identify which employees need to take a break or sleep, it is overworked, it needs to transfer virtual computers, and it moves virtual machines. The EEH structure also includes a move computation that assigns new workers to move virtual machines. In terms of power utilization efficiency (PUE), data center energy productivity (DCEP), usual run time, throughput, and power consumption, the replication test results demonstrated that the EEH system was one way to reduce power consumption has demonstrated superiority over competing approaches that leverage cost savings to address.

    Keyword

    Energy-Efficient half (EEH), Power Utilization Efficiency (PUE), Data Center Energy Productivity (DCEP)


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ISSN 1004-9037

         

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