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VOL. 6, ISSUE 2 (2021)
Load balancing in cloud computing using Multi-objective Bio-inspired techniques - A comprehensive study
Authors
Brototi Mondal
Abstract
A significant issue in cloud computing is load balancing, which makes
it difficult to guarantee the appropriate operation of services related to
Quality of Service, performance evaluation. Mapping workloads to utilize
computer resources that considerably increase performance is the main goal of
load balancing. Due to the huge solution space, load balancing in cloud
computing is classified as a "NP-hard" topic. As a result, predicting
the ideal outcome takes more time. There aren't many strategies that might
possibly produce a polynomial-time optimal solution to these problems. For
those sorts of problems, it has been demonstrated in prior studies that
metaheuristic-based techniques may produce correct results in a respectable
amount of time. Based on performance metrics such as makespan time, degree of
imbalance, response time, data center processing time, and resource consumption, this article offers
a comparative analysis of several metaheuristic load balancing methods for
cloud computing. Based on performance criteria, it demonstrates how different
Meta-heuristic Load Balancing approaches perform. Numerous approaches including
evolutionary and non-evolutionary algorithms have been suggested to develop
efficient procedures and algorithms for allocating the clients’s requests to
reachable Cloud nodes. This study's objective is to investigate
various load balancing algorithms which reduce response time while maximizing
resource use. The assessed algorithms are categorized in accordance with a
thorough taxonomy that is focused on load balancing techniques in cloud data
centers. The different techniques are demonstrated and compared also.
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Pages:34-41
How to cite this article:
Brototi Mondal "Load balancing in cloud computing using Multi-objective Bio-inspired techniques - A comprehensive study". International Journal of Advanced Science and Research, Vol 6, Issue 2, 2021, Pages 34-41
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