EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Vivek Shrivastava; Yasmin Shaikh

doi:10.17485/IJST/v15i37.799

Article

EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i37.799

Year: 2022, Volume: 15, Issue: 37, Pages: 1850-1858

Original Article

EBTASIC: An Entropy-Based TOPSIS Algorithm for Task Scheduling in IaaS Clouds

Vivek Shrivastava^1*, Yasmin Shaikh¹

¹International Institute of Professional Studies, Devi Ahilya University, Indore-452001, Madhya Pradesh, India

*Corresponding Author
Email: [email protected]

Received Date:12 April 2022, Accepted Date:17 August 2022, Published Date:26 September 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To propose an algorithm to balance resource utilization and revenue generation in the cloud environment. Methods: This study proposes the Entropy-Based TOPSIS algorithm for task scheduling in IaaS Clouds (EBTASIC) to balance resource utilization and revenue generation using the objective-based Entropy Weighting Method (EWM) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Findings/Novelty: Various performance evaluation factors are calculated using EBTASIC and compared with baseline algorithms First Come First Serve (FCFS) and Earliest Deadline First (EDF) algorithms over 12750 lease requests with hard deadlines. The actual response time of EBTASIC is 37.61 percent faster than FCFS and 47.95 percent faster than EDF. Total time spent on lease execution by EBTASIC is reduced by 3.43 percent when compared to FCFS and 3.99 percent when compared to EDF. Turnaround time of EBTASIC is lowered by 21.14 percent when compared to FCFS and 28.81 percent when compared to EDF. EBTASIC throughput is enhanced by 40.80 percent over FCFS and 54.3 percent over EDF. The average response time of EBTASIC is shortened by 9.38 percent compared to FCFS and 14.81 percent compared to EDF. Resource utilization of the proposed algorithm EBTASIC is enhanced by 25.54 percent over FCFS and 6.79 percent over the EDF algorithm.

Keywords: Task and VM Scheduling; MCDM Techniques; TOPSIS; Entropy Weighting Method; EBTASIC Algorithm

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Copyright

© 2022 Shrivastava & Shaikh. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Published By Indian Society for Education and Environment (iSee)