Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing

Cloud computing enables a new supplement of consumption and delivery model for internet based services and protocol. It helps to provide software, hardware and data in form of collaborative services on the demand of the end user. To meet the QoS and ensure high interoperability and scalability is one of the most challenging tasks for cloud service provider. However, there are also several technical challenges that need to be tackled before the benefits can be fully realized. Among them reliability, resource provisioning, and efficient resources consuming etc are major concern. Load balancing also one of them. It includes selecting a proper node that must be full filled end user demand and also distribution of dynamic workload evenly into the multiple nodes. So load balancing can be described as an optimization problem and should be adapting nature due to the changing needs. In this paper we suggest a novel load balancing strategy to search under loaded node to balance load from overwhelmed node. CloudAnalyst used as a simulation tool for the proposed load balancing strategy. Experimental results of the sample application are really very encouraging. Significantly the results of the proposed algorithm are compared and outperformed the traditional strategy like First Come First Serve(FCFS), local search algorithm like Stochastic Hill Climbing(SHC) and soft computing approaches like Genetic Algorithm (GA) and Ant Colony Optimization(ACO).