Indian Journal of Science and Technology

Abstract

Objective: The main objective of this research is to allocate the resources with high profit and achieve high user satisfaction level in the cloud computing environment. Methods: An innovative technique called Position Balanced Parallel Particle Swarm Optimization (PB-PPSO) method is introduced for allocating resources. The main intent of PB-PPSO is to find the optimized resources for the set of tasks with less make span and minimum price. The set of rules are generated from the optimized resources for the training process. In the testing process, the resources are allocated to the new users by learning the rules from the training process. Results: PB-PPSO method shows high profit when compared to the existing methods such as Support Vector Machines (SVM) and Artificial Neural Network (ANN). In the PB-PPSO method, the optimized set of resources is determined for the set of tasks by using the particle swarm optimization algorithm. Then the rules are generated for the classification process. If the arrival rate of users is 500, the total profit is 720$ and the response time is 78ms. Based on the comparison and the results from the experiment shows the proposed approach works better than the other existing systems with high profit and less average response time. Conclusion: The findings demonstrate that the PB-PPSO is presented and this method has high efficiency in terms of total profit and average response time for allocating the resources for the users.
Keywords: artificial neural network, Cloud Computing, Position Balanced Parallel Particle Swarm Optimization, Resource Allocation, Support Vector Machines