Design of A Multi-Constraint PSO for Resource Allocation and Task Scheduling

Authors

  • Rajkumar Kalimuthu Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, India.
  • Brindha Thomas Department of Information Technology, Noorul Islam Centre for Higher Education, India.

Keywords:

cloud computing, task scheduling, resource allocation, rule generation, randomness

Abstract

Cloud computing (CC) is a modern technology where resource allocation and task scheduling are considered as an essential factor. Based on the literature, particle swarm optimization (PSO) is a stochastic optimization approach inspired by the foraging nature of bird flocks. PSO is extensively used in various fields like scheduling cloud resources, scheduling problems, etc. The efficiency of the model intends to address the issues encountered in existing approaches. Here, time-slot-based rule generation (TS-RG) is designed to handle workflow scheduling in the cloud. A particle scrambling process is provided to map the VM for every task and perform scheduling sequentially. An idle time slot-aware re-scrambling process is anticipated to re-scramble the particles to various scheduling solutions. Due to PSO randomness, the cloud encounters invalid task priorities; however, this issue is handled effectually by the repair method used for handling the invalid task priorities and makes them valid. The anticipated model is compared with various prevailing approaches, and the experimental outcomes demonstrate that the anticipated model outperforms other works in deadline fulfilment and execution cost computation.

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Published

05.12.2023

How to Cite

Kalimuthu, R. ., & Thomas, B. . (2023). Design of A Multi-Constraint PSO for Resource Allocation and Task Scheduling. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 426–440. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4091

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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