Power-Based Single Threshold Load Balancing Algorithm For Cloud Computing

Dharmeshkumar B. Bhavsar; ParimalkumarPrafulchandra Patel; Surbhi Syal; Shafqat Ul Ahsaan; B Murali Krishna; Arjun Kotwal

doi:10.14569/IJACSA.2015.060924

Author(s)

Dharmeshkumar B. Bhavsar Chimanbhai Patel Institute of Computer Applications, (SVG University,Ahmedabad), Gujarat,India

ParimalkumarPrafulchandra Patel Khyati School of Computer Application,Ahmedabad, Gujarat,India

Surbhi Syal Department of Computer Science Engineering, Manipal University Jaipur,Rajasthan,India

Shafqat Ul Ahsaan Department of Computer Science, School of Computing and Artificial Intelligence, NIMS University Rajasthan,Jaipur,India

B Murali Krishna Department of Computer Science & Engineering, Shri Venkateshwara University,Gajraula, UP,India

Arjun Kotwal Department of Computer Application, PSPS Govt. College for Women,Gandhi Nagar, Jammu (J&K),India

Abstract

Cloud Computing has become a compelling concept in today's IT industry, enabling the development and deployment of custom applications across various domains using a pay-as-you-go model. This model supports hosting pervasive applications from consumer, scientific, and business sectors. Efficient resource utilization in cloud environments is crucial to prevent wastage due to underutilization and to avoid prolonged response times caused by over-utilization. A significant challenge in cloud computing is the scheduling of user requestsspecifically, how to allocate resources effectively to ensure tasks are completed quickly while maximizing resource efficiency. Load balancing plays a pivotal role in resource provisioning for high-availability cloud solutions. In this context, a Single Threshold Power-Based Load Balancing Algorithm has been proposed for cloud computing environments. The algorithm's fitness function is based solely on bandwidth to select the optimal virtual machine (VM) for task execution. The algorithm's performance is evaluated using Cloud Reports, Cloud Analyst Simulators, and MATLAB, focusing on key metrics such as power consumption, simulation time, CPU utilization, and cloudlet processing per data center. Additionally, the algorithm's performance is compared against the Single Threshold Algorithm, tested under two broker policies: time-shared and spaceshared.

Publication Details

Page(s) 278-288

DOI 10.14569/IJACSA.2015.060924

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 3, Year: 2024

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Keywords

resource allocation, Cloud computing, Resource Utilization, Virtual Machine, Cloud Services Cloudlet

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