An Empirical Investigation on the Simulation of Priority and Shortest-Job-First Scheduling for Cloud-Based Software Systems

Background: Given the dynamics in resource allocation schemes offered by cloud computing, effective scheduling algorithms are important to utilize these benefits. Aim: In this paper, we propose a scheduling algorithm integrated with task grouping, priority-aware and SJF (shortest-job-first) to reduce the waiting time and make span, as well as to maximize resource utilization. Method: Scheduling is responsible for allocating the tasks to the best suitable resources with consideration of some dynamic parameters, restrictions and demands, such as network restriction and resource processing capability as well as waiting time. The proposed scheduling algorithm is integrated with task grouping, prioritization of bandwidth awareness and SJF algorithm, which aims at reducing processing time, waiting time and overhead. In the experiment, tasks are generated using Gaussian distribution and resources are created using Random distribution as well as CloudSim framework is used to simulate the proposed algorithm under various conditions. Results are then compared with existing algorithms for evaluation. Results: In comparison with existing task grouping algorithms, results show that the proposed algorithm waiting time and processing time decreased significantly (over 30%). Conclusion: The proposed method effectively minimizes waiting time and processing time and reduces processing cost to achieve optimum resources utilization and minimum overhead, as well as to reduce influence of bandwidth bottleneck in communication.