AN EFFICIENT RESOURCE MANAGEMENT FRAMEWORK USING NON SORTED GENETIC ALGORITHM (NSGA-II) AND PARALLEL GENETIC ALGORITHM (PGA)

Cloud computing plays a major role in infrastructure and infrastructural cost reduction. Present day cloud computing service providers provides different service models for different users based on their need. These service models are cost effective and provide significant impact to their user on their resource usage. Most of the time, resource allocation and scheduling of the resources are important in order to reduce the resource wastage. To address the above mentioned issues, this paper proposes a novel resource allocation and scheduling framework. The key idea is to utilize the existing optimization algorithm called NSGA-II and modifying the algorithm to work parallel computing using parallel genetic algorithm. In this proposed work, various investigations such as efficient placement of virtual machine on the physical machine is being carried out. The efficient parallel genetic algorithm sorts the scheduling problem, which is faster than the traditional genetic algorithm. The experimental results obtained shows that the proposed framework improves the resource allocation, scheduling, faster in allocation and reduced unavailability requests.