Abstract
As a new computing paradigm, cloud computing is receiving considerable attention in both industry and academia. Task scheduling plays an important role in large-scale distributed systems. However, most previous work only consider cost or makespan as optimized objective for cloud computing. In this paper, we propose a soft real-time task scheduling algorithm based on particle swarm optimization approach for cloud computing. The optimized objectives include not only cost and makespan, but also deadline missing ratio and load balancing degree. In addition, to improve resource utilization and maximize the profit of cloud service provider, a utility function is employed to allocate tasks to machines with high performance. Simulation results show the proposed algorithm can effectively minimize deadline missing ratio, maximize the profit of cloud service provider and achieve better load balancing compared with baseline algorithms.
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Acknowledgment
Thank anonymous reviewers for their valuable suggestions. This work is partly supported by the National Natural Science Foundation of China under Grant No. 61103175, the Fujian Province Key Laboratory of Network Computing and Intelligent Information Processing Project under Grant No. 2009J1007.
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Chen, H., Guo, W. (2015). Real-Time Task Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization. In: Qiang, W., Zheng, X., Hsu, CH. (eds) Cloud Computing and Big Data. CloudCom-Asia 2015. Lecture Notes in Computer Science(), vol 9106. Springer, Cham. https://doi.org/10.1007/978-3-319-28430-9_11
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DOI: https://doi.org/10.1007/978-3-319-28430-9_11
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