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Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds

Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds

Djouhra Dad, Ghalem Belalem
Copyright: © 2020 |Volume: 10 |Issue: 3 |Pages: 15
ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781799807759|DOI: 10.4018/IJCAC.2020070105
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MLA

Dad, Djouhra, and Ghalem Belalem. "Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds." IJCAC vol.10, no.3 2020: pp.81-95. http://doi.org/10.4018/IJCAC.2020070105

APA

Dad, D. & Belalem, G. (2020). Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds. International Journal of Cloud Applications and Computing (IJCAC), 10(3), 81-95. http://doi.org/10.4018/IJCAC.2020070105

Chicago

Dad, Djouhra, and Ghalem Belalem. "Efficient Strategies of VMs Scheduling Based on Physicals Resources and Temperature Thresholds," International Journal of Cloud Applications and Computing (IJCAC) 10, no.3: 81-95. http://doi.org/10.4018/IJCAC.2020070105

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Abstract

Cloud computing offers a variety of services, including the dynamic availability of computing resources. Its infrastructure is designed to support the accessibility and availability of various consumer services via the Internet. The number of data centers allow the allocation of the applications, and the process of data in the cloud is increasing over time. This implies high energy consumption, thus contributing to large emissions of CO2 gas. For this reason, solutions are needed to minimize this power consumption, such as virtualization, migration, consolidation, and efficient traffic-aware virtual machine scheduling. In this article, the authors propose two efficient strategies for VM scheduling. SchedCT approach is based on dynamic CPU utilization and temperature thresholds. SchedCR approach takes into consideration dynamic CPU utilization, RAM capacity, and temperature thresholds. These approaches have efficiently decreased the energy consumption of the data centers, the number of VM migrations, and SLA violations, and this reduces, therefore, the emission of CO2 gas.

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