Open Access

ARTICLE

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

by K. Rajakumari^1,*, M.Vinoth Kumar², Garima Verma³, S. Balu⁴, Dilip Kumar Sharma⁵, Sudhakar Sengan⁶

1 Department of Computer Science and Engineering, School of Engineering, Avinashlingam Institute for Home Science and Higher Education for Women, Coimbatore, 641043, Tamil Nadu, India
2 Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bangalore, 560082, India
3 School of Computing, DIT University, Dehradun, 248009, Uttarakhand, India
4 Department of Computer Science and Engineering, Paavai Engineering College, Pachal, 637018, Tamil Nadu, India
5 Department of Mathematics, Jaypee University of Engineering and Technology, Guna, 473226, M.P., India
6 Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, 627152, Tamil Nadu, India

* Corresponding Author: K. Rajakumari. Email:

Computer Systems Science and Engineering 2022, 40(2), 581-592. https://doi.org/10.32604/csse.2022.019175

Received 05 April 2021; Accepted 10 May 2021; Issue published 09 September 2021

Abstract

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.

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Citations