Resource Scheduling Using Modified FCM and PSO Algorithm in Cloud Environment

Abstract

Cloud computing is a growing environment in the IT industry. Many of the users are interested to outsource their data in cloud. However, load balancing in cloud is still at risk. Resource allocation plays a major role in load balancing. In this scheduling problem, independent tasks in cloud computing can allocate resources by the use of fuzzy c means algorithm (FCM). To allocate tasks to their corresponding resources, particle swarm optimization algorithm (PSO) is used. This paper proposes a hybridization of the FCM and PSO algorithm which is called H-FCPSO algorithm. FCM uses Euclidean distances and PSO optimizes the cluster centers. FCM requires the number of clusters used in advance and thus PSO comes into action to find the number of best clusters. Hence, H-FCPSO identifies the number of clusters and enhances the load balancing. Since our proposed system selects resources based on parallel execution kit reduces the load imbalance in cloud. When compared to Genetic algorithm (GA), Ant Colony Optimization algorithm (ACO), PSO algorithm showed better results in terms of memory. Similarly, FCM was compared with k-means clustering algorithm, Hierarchial algorithm and it showed outputs with better accuracy. The proposed system evaluated data sets and proved to overcome the issues in load balancing and load scheduling which is proved by its precision in the outputs.