Modified Genetic Based Algorithm for Load Balancing in Cloud Computing

Abstract

Cloud-based Computation is one of the important computing technologies where demand-based services and resources from the user's application to cloud data center with the help of internet connection can be achieved as pay on demand. The software, resources, applications are shared among different computing devices in a cloud-based approach. Balancing of Load in cloud computation technology is meant for the distribution of workload to balance loads among different cloud servers connected with different virtual machines. The main idea of load balancing is to optimize resource usage, cost of data center and virtual machines, maximization of throughput, reduction of response time and avoidance of overloading in different virtual machines as well as cloud servers. In cloud load balancing fitness checking of different cloud servers as well as virtual machines are playing an important role in cloud applications. In this proposed research paper genetic-based algorithmic approach has been used to handle the load balancing in the cloud environment. Genetic Algorithm can be used for getting solution of an optimization problem. Here it is used to find the fittest virtual machines connected with different Data Centers. Our proposed work is more appropriate compared to different algorithms discussed in [1][2], as in proposed algorithm cloudlets are taking a smaller amount time for execution and performs the load balancing in cloud environment with more efficient way by using strong fittest function for allocating cloudlets into appropriate virtual machines of a data center in cloud environment.