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SSEPC Cloud: Carbon Footprint Aware Power Efficient Virtual Machine Placement in Cloud Milieu
Computer Science and Information Systems, Tome 21 (2024) no. 3 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

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The consumption of energy and carbon emission in cloud datacenters are the alarming issues in recent times, while optimizing the average response time and service level agreement (SLA) violations. Handful of researches have been conducted in these domains during virtual machine placement (VMP) in cloud milieu. Moreover it is hard to find researches on VMP considering the cloud regions and the availability zones along with the datacenters, although both of them play significant roles in VMP. Hence, we have worked on a novel approach to propose a hybrid metaheuristic technique combining the salp swarm optimization and emperor penguins colony algorithm, i.e. SSEPC to place the virtual machines in the most suitable regions, availability zones, datacenters, and servers in a cloud environment, while optimizing the mentioned quality of service parameters. Our suggested technique is compared with some of the contemporary hybrid algorithms in this direction like Sine Cosine Algorithm and Salp Swarm Algorithm (SCA-SSA), Genetic Algorithm and Tabu-search Algorithm (GATA), and Order Exchange Migration algorithm and Ant Colony System algorithm (OEMACS) to test its efficacy. It is found that the proposed SSEPC is consuming 4.4%, 8.2%, and 16.6% less energy and emitting 28.8%, 32.83%, and 37.45% less carbon, whereas reducing the average response time by 11.43%, 18.57%, and 26% as compared to its counterparts GATA, OEMACS, and SCA-SSA respectively. In case of SLA violations, SSEPC has shown its effectiveness by lessening the value of this parameter by 0.4%, 1.2%, and 2.8% as compared to SCA-SSA, GATA, and OEMACS respectively.
Keywords: Virtual Machine Placement, Energy Consumption, Carbon Emission, Salp Swarm Optimization, Emperor Penguins Colony Algorithm 
@article{CSIS_2024_21_3_a5,
     author = {Bivasa Ranjan Parida and Amiya Kumar Rath and Bibudhendu Pati and Chhabi Rani Panigrahi and Hitesh Mohapatra and Tien-Hsiung Weng and Rajkumar Buyya},
     title = {SSEPC {Cloud:} {Carbon} {Footprint} {Aware} {Power} {Efficient} {Virtual} {Machine} {Placement} in {Cloud} {Milieu}},
     journal = {Computer Science and Information Systems},
     year = {2024},
     volume = {21},
     number = {3},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a5/}
}
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%A Bibudhendu Pati
%A Chhabi Rani Panigrahi
%A Hitesh Mohapatra
%A Tien-Hsiung Weng
%A Rajkumar Buyya
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Bivasa Ranjan Parida; Amiya Kumar Rath; Bibudhendu Pati; Chhabi Rani Panigrahi; Hitesh Mohapatra; Tien-Hsiung Weng; Rajkumar Buyya. SSEPC Cloud: Carbon Footprint Aware Power Efficient Virtual Machine Placement in Cloud Milieu. Computer Science and Information Systems, Tome 21 (2024) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a5/