Improved GA using population reduction for load balancing in cloud computing

Cloud computing is a new hype in computer industry, which has different thoughts by different researchers. But beyond those thoughts, cloud has some limitation also which needs to be more focused. Basically cloud is based on use par pay scenario identified by user's services. But for each and every rewarding, that services cloud needs some predefine requirement circumstances to follow which affect different parameters like response time, resource utilization, balancing load, indexing of resources as well as jobs & etc. Lots of soft computing techniques like genetic, honey bee, stochastic hill climbing, and ant colony, throttled and other algorithm are used to please those parameters to improve the scheduling of resources as well as jobs in cloud environment. Our proposed work focused on utilization of resource and response time based on genetic algorithm but we modified that genetic algorithm with the help of partial population reduction method that will help to satisfy the request of user services.