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A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing
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Author(s): Subhadarshini Mohanty (Siksha ‘O' Anusandhan University, Department of Computer Science and Engineering, Bhubaneswar, India), Prashanta Kumar Patra (College of Engineering and Technology, Department of Computer Science and Engineering, Bhubaneswar, India), Mitrabinda Ray (Siksha ‘O' Anusandhan University, Department of Computer Science and Engineering, Bhubaneswar, India)and Subasish Mohapatra (College of Engineering and Technology, Department of Computer Science and Engineering, Bhubaneswar, India)
Copyright: 2021
Pages: 23
Source title:
Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-5339-8.ch023
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Abstract
Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization. Performance comparisons are made with Genetic Algorithm (GA) and other popular algorithms on different measures like makespan calculation and resource utilization. Different cloud configurations are considered with varying Virtual Machines (VMs) and Cloudlets to analyze the efficiency of proposed algorithm. The proposed approach performs better than existing schemes.
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