An Intelligent Task Scheduling Approach for Cloud Using IPSO and A* Search Algorithm

Abstract

Cloud computing technology is playing a vital role in this fast internet era for transferring, storing and accessing the large volume of confidential data which are official, medical and military. Efficient techniques for searching and processing the cloud data are essential for providing better service to the cloud users. For the fast processing and searching the data, many techniques were proposed by various researchers in the past. However, those techniques are not working in better results in cloud services. In a heterogeneous environment, achieving higher efficiency is an important issue in task scheduling. To solve this problem, many evolutionary algorithms have been adopted in the past. Even though it is a Nondeterministic Polynomial-hard problem, the local search algorithms are integrated for increasing convergence speed in population-based algorithms. In this paper, we propose a new task scheduling approach which combines an incremental particle swarm optimization and A * search algorithm for effective task scheduling. Moreover, the current particle swarm optimization algorithms and the heuristic algorithms gained in results on random and scientific Directed Acyclic Graph. The experiments show that the performance of the proposed approach is better when it is compared with the existing task scheduling approaches.