Performance Evaluation of Various VM Migration based Nature-inspired Mechanisms in Cloud Environment
Main Article Content
Abstract
The number of cloud users is rapidly growing in a cloud computing environment, which increases the need for resources. Virtual machine migration, which involves moving the overloaded host to another one, can be used to handle the growing demand for resources successfully. The Bat method, PSO (Particle Swarm Optimization), Cuckoo Search (CS), and Genetic algorithm (GA) are some of the popular meta-heuristics algorithms used in this paper to minimize migration time and makespan value of Virtual Machine(VM) Migration. The main goals of this work are to accomplish VM migration with shorter migration times, smaller makespan values, and higher VM throughput values. Additionally, compare the methods' performance to determine which algorithm is more efficient for minimizing migration time in cloud environments. The fitness value, migration time, makespan, and throughput performance characteristics have been calculated for various task sizes and execution iterations. According to calculated performance characteristics, the Bat algorithm outperformed the other three. The Bat algorithm's migration time is better by 2% to PSO, 6% compared to Cuckoo Search, and 50% compared to GA. Also, when performing VM migration in a cloud computing environment, the Bat algorithm outperforms PSO, CS, and GA in terms of makespan, fitness value, and throughput value.
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