Abstract
With the development of the Internet of Things (IoT), more and more applications are increasingly demanding latency. Traditional single-task scheduling strategy is difficult to satisfy low-latency demand. This is because the task scheduler usually schedules tasks to a closer server, which leads to an increase in task latency when there are more tasks, which in turn leads to an increase in task rejection rate. In this paper, we propose an end-edge cooperative multi-tasks scheduling (MTS) strategy based on improved particle swarm optimization (IPSO) algorithm. At first, we design a Software-Defined Networks controller algorithm to cluster task offload requests. Then, we set the scheduling priority for the multi-task clusters. At last, we minimize the total offloading cost of total tasks as the optimization goal to satisfy its delay. The results demonstrate that the strategy we proposed can effectively reduce the service cost of the system, and the processing delay of tasks, which improves the success rate of task processing.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 61972140 and 62002109, and the National Defense Basic Research Plan under Grant JCKY2018110C145.
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Li, F., Qiao, Y., Luo, J., Yin, L., Liu, X., Fan, X. (2022). End-Edge Cooperative Scheduling Strategy Based on Software-Defined Networks. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13473. Springer, Cham. https://doi.org/10.1007/978-3-031-19211-1_36
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DOI: https://doi.org/10.1007/978-3-031-19211-1_36
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