A Synthetic Heuristic Algorithm for Independent Task Scheduling in Cloud Systems

In this paper, we present a synthetic method based on genetic algorithm, for independent task scheduling in cloud computing systems. Task scheduling is a major issue in large-scale distributed systems that impresses on system performance. For some reasons such as heterogeneous and dynamic features in cloud environment, task scheduling has appeared as a NPcomplete problem. Our proposed algorithm (SHIS), by some goal oriented operations such as, making an optimize initial population, dual step evaluation, and also, running the tasks by a special ordering considering resource load balancing and quality of service, achieves the optimize makespan. It also decreases the probability of task failure rate on running, based on the resource failure frequency rate, and also decreases the task starvation problem. It supports the scheduling for new entered tasks in system by a dynamic method. The experimental results show that the proposed method solution is better than the other studied algorithms.