A Comparison between PSO and GA Approach for Energy and Cost Effective Saas Placement on Cloud

摘要

Software as a Service (SaaS) is a way of delivering applications over the Internet as a service. SaaS applications are sometimes called Web-based software, on-demand software, or hosted software. It is the very important services in cloud computing. The challenges in Saas placement problem depends resource requirements, cloud network size and communication among its components. This paper is a comparative study of two saas placement algorithms, the aim is to finds energy efficient cost effective solution for Saas Placement Problem (SPP). In this work, it find optimal Saas placement in Cloud based on service level agreement (SLA). Particle Swarm Optimization(PSO) and Genetic Algorithm (GA) that have been applied to find the optimal placement of Saas component and find which algorithm approach minimize the total cost incurred to the Saas provider and minimize the total energy. Virtual machine placement approaches to the virtual machine placement problem consider the energy consumption by physical machines in a data center. Experimental results show that energy efficient Saas Placement using PSO generates better solutions than Saas Placement using GA.