Abstract
Nowadays Cloud Computing is an emerging technology in the area of parallel and distributed computing. Task scheduling is one of the major issues in cloud computing, which plays an important role to improve the overall performance and services of the cloud. Task scheduling in cloud computing means assign best suitable resources for the task to be executed with the consideration of different parameters like execution time, user priority, cost, scalability, throughput, makespan, resource utilization and so on. In this paper, we address the challenge of task scheduling, and we consider one of most critical issues in scheduling process such as the task priorities. The goal of this paper is to propose an efficient Dynamic Priority-Queue (DPQ) algorithm based on Analytic Hierarchy Process (AHP) with Particle Swarm Optimization (PSO) algorithm. The proposed algorithm DPQ-PSO gives full consideration to the dynamic characteristics of the cloud computing environment. Further, the proposed algorithm has been validated through the CloudSim simulator. The experimental results validate that the proposed approach can effectively achieve good performance, user priority, load balancing, and improve the resource utilization.
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Ben Alla, H., Ben Alla, S., Ezzati, A., Touhafi, A. (2017). An Efficient Dynamic Priority-Queue Algorithm Based on AHP and PSO for Task Scheduling in Cloud Computing. In: Abraham, A., Haqiq, A., Alimi, A., Mezzour, G., Rokbani, N., Muda, A. (eds) Proceedings of the 16th International Conference on Hybrid Intelligent Systems (HIS 2016). HIS 2016. Advances in Intelligent Systems and Computing, vol 552. Springer, Cham. https://doi.org/10.1007/978-3-319-52941-7_14
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