Open Access

ARTICLE

Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

Prasanna Kumar Kannughatta Ranganna¹, Siddesh Gaddadevara Matt², Chin-Ling Chen^3,4,*, Ananda Babu Jayachandra⁵, Yong-Yuan Deng^4,*

1 Department of Computer Science & Engineering, Ramaiah Institute of Technology, Bangalore, 560054, India
2 Department of Artificial Intelligence & Data Science, Ramaiah Institute of Technology, Bangalore, 560054, India
3 School of Information Engineering, Changchun Sci-Tech University, Changchun, 130600, China
4 Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, 41349, Taiwan
5 Department of Information Science and Engineering, Malnad College of Engineering, Hassan, 573202, India

* Corresponding Authors: Chin-Ling Chen. Email: ; Yong-Yuan Deng. Email:

Computers, Materials & Continua 2024, 80(2), 2557-2578. https://doi.org/10.32604/cmc.2024.051634

Received 11 March 2024; Accepted 08 July 2024; Issue published 15 August 2024

Abstract

In recent decades, fog computing has played a vital role in executing parallel computational tasks, specifically, scientific workflow tasks. In cloud data centers, fog computing takes more time to run workflow applications. Therefore, it is essential to develop effective models for Virtual Machine (VM) allocation and task scheduling in fog computing environments. Effective task scheduling, VM migration, and allocation, altogether optimize the use of computational resources across different fog nodes. This process ensures that the tasks are executed with minimal energy consumption, which reduces the chances of resource bottlenecks. In this manuscript, the proposed framework comprises two phases: (i) effective task scheduling using a fractional selectivity approach and (ii) VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO). The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local exploitation. This balance enables the use of a wide range of solutions that leads to minimal total cost and makespan, in comparison to other traditional optimization algorithms. The FSCPSO algorithm’s performance is analyzed using six evaluation measures namely, Load Balancing Level (LBL), Average Resource Utilization (ARU), total cost, makespan, energy consumption, and response time. In relation to the conventional optimization algorithms, the FSCPSO algorithm achieves a higher LBL of 39.12%, ARU of 58.15%, a minimal total cost of 1175, and a makespan of 85.87 ms, particularly when evaluated for 50 tasks.

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Keywords

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