A Multi Objective Virtual Machine Placement Method for Reduce Operational Costs in Cloud Computing by Genetic

Increase of demand in using cloud computing caused increase of operational costs consumption energy and resources consumed. As a result moreover satisfying services with quality requested through service level agreement SLA, we must reduce consuming energy and cost resulting from resources used. According to too much researches conducted on reducing consumption energy, in this article we will focus on reducing operational costs resulting from wasting resources using technique of mapping virtual machines to service providers whose the most important one is using on demand provisioning model which will prevent from wasting resources and more exploitation of cloud computing and reduction in operational costs. One of the key aspects of the process is considering load feature of virtual machines. But most tasks done in this field do not care about it and on the other hand only optimization is noticed. Due to existence of faults and also high level of exploitation of genetic algorithms in finding fine results in searching space, in this article a method was presented based on multi-purpose genetic algorithm, where by considering task feature of virtual machines. Several objectives are considered for optimizing during mapping process. Comparing results gained from algorithm suggested with a random algorithm and one algorithm in the first multipurpose choice, we can come to this conclusion that the algorithm suggested will establish better results on providence in resources and reducing operational costs.