Abstract
In edge computing, in order to obtain low latency and efficient service, users usually offload tasks from their devices to the nearby edge cloud for processing. How to schedule these tasks to the edge cloud efficiently and reliably is of particular interest to edge computing. In this paper, we design an online parallel tasks scheduling algorithm based on the theory of reinforcement learning, which not only saves the cost of edge server processing tasks, but also makes further optimization in ensuring the timeliness of task completion and shortening the response time. Finally, we conduct simulation experiments based on real data sets, compare our algorithm with existing algorithms in many aspects, and our algorithm is shown to be efficient and reliable.
This work was supported in part by the Anhui University Natural Science Foundation-funded project under Grant KJ2019A0035, in part by the Nature Science Program of Anhui Province under Grant 1908085MF181.
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Lu, J., Guo, X., Zhao, Xg., Zhou, H. (2020). A Parallel Tasks Scheduling Algorithm with Markov Decision Process in Edge Computing. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_27
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DOI: https://doi.org/10.1007/978-3-030-64243-3_27
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