Scheduling jobs using oppositional-GSO algorithm in cloud computing environment

Cloud computing is an emerging domain that requires more algorithm and techniques for various process. The scheduling process in cloud computing platform needs a good algorithm to schedule the jobs of different users. The main objective of this approach is to develop a scheduling algorithm through iterative algorithm. Here, we use oppositional group search optimization algorithm for iterative process in cloud computing. Initially, we generate a population that contains a group of members and the members consist of the number of users and their respective jobs. The motto of our research is to schedule the user given jobs efficiently. We separate the members from the population based on the fitness function to perform different operations and to generate new members. We calculate the fitness for the new members and iterate the process until we get a stable best member for repeated iteration. Then, we schedule the jobs for the users based on the best member obtained.