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SSEPC Cloud: Carbon Footprint Aware Power Efficient Virtual Machine Placement in Cloud Milieu

Bivasa Ranjan Parida¹, Amiya Kumar Rath^{1, 2}, Bibudhendu Pati³, Chhabi Rani Panigrahi³, Hitesh Mohapatra⁴, Tien-Hsiung Weng⁵ and Rajkumar Buyya⁶

1. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology,
   Burla, Odisha, India
   bivasa.parida@silicon.ac.in
2. Vice-Chancellor, Biju Patnaik University of Technology, Rourkela, Odisha, India
   akrath_cse@vssut.ac.in
3. Department of Computer Science, Rama Devi Women’s University, Bhubaneswar, Odisha, India
   patibibudhendu@rdwu.ac.in panigrahichhabi@gmail.com
4. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India
   hiteshmahapatra@gmail.com
5. Department of Computer Science and Information Engineering, Providence University, Taiwan
   thweng@gm.pu.edu.tw
6. Cloud Computing and Distributed Systems (CLOUDS) Lab, School of Computing and
   Information Systems, University of Melbourne, Australia
   rbuyya@unimelb.edu.au

Abstract

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.

Key words

Virtual Machine Placement, Energy Consumption, Carbon Emission, Salp Swarm Optimization, Emperor Penguins Colony Algorithm

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS230923018P

Publication information

Volume 21, Issue 3 (June 2024)
Year of Publication: 2024
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Parida, B. R., Rath, A. K., Pati, B., Panigrahi, C. R., Mohapatra, H., Weng, T., Buyya, R.: SSEPC Cloud: Carbon Footprint Aware Power Efficient Virtual Machine Placement in Cloud Milieu. Computer Science and Information Systems, Vol. 21, No. 3, 759-600. (2024), https://doi.org/10.2298/CSIS230923018P