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Improvised Genetic Approach for an Effective Resource Allocation in Cloud Infrastructure

Allocation and schedule of virtual machines based on the requisite of cloud users is a challenging crucial chore in cloud services especially in IaaS (Infrastructure as a Service). Whenever the virtual machines requests are increased or decreased, the resources have to be balanced to attain optimal resource utilization. In this paper, we propose an approach namely Effective Cloud Resource Allocation Using Improvised Genetic Approach, which directs to accomplish better virtual machine allocation across cloud servers for maintaining vertical elasticity and minimizing response time. The proposed approach is focused on elasticity and Scheduling to improve resource allocation mechanism in cloud computing. This paper not only focuses the resource utilization problem, but also discusses our innovative algorithm called Enhanced Genetic Algorithm (EGA) using Multipurpose Mutation Operator. The proposed algorithm makes the effectual use of mutation operator to avoid local optimum problem. It repairs infeasible solutions and handles local search efficiently. The result shows that the EGA provides an optimal solution and proves better performance compared to the existing algorithms. Our method exemplifies that there is a substantial improvement in response time and also reduction in VM (Virtual Machine) migration count. Keywords— Cloud Computing, Virtualization, Elasticity, Resource Allocation, Scheduling, Genetic Algorithm.