Hybrid Bat and Genetic Algorthim Approach for Cost Effective SaaS Placement in Cloud Environment

Abstract

The increasing demand of software service in cloud environment needs strategic placement in the cloud infrastructure. Thus, in which the users use the service based on the service model of the provider and pays based on their use of the resources. These resources are storage, memory processing element and bandwidth. Efficient optimal placement is the main issue in order to provide a cost effective service to the user. This research has proposed hybrid approaches to addresses the initial software task placement problem by exploring the advantage of both Bat algorithm (BA) and Genetic algorithm (GA), to make the initial ST placement processes optimum and cost effective. In order to evaluate the performance of the proposed hybrid algorithms, an experimental environment had configured using CloudSim simulation tool. The proposed solution performance has evaluated by compared with those existing placement algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization algorithm (PSO). According to the result the proposed algorithm has reduced the placement cost up to 2 −13% on a cloud environment.