Abstract
Computing resources of mobile devices are growing, and unoccupied resources can be shared to provide support for edge computing services in edge clouds. Unlike stable servers, a significant challenge is that mobile devices may exit or join an edge cloud at any time due to change of position. This dynamic nature of mobile devices may result in abortions of task execution. In this paper, a risk-aware task assignment scheme called RATA is proposed. RATA minimizes the overhead caused by potential abortions of task execution by prioritizing tasks to the edge nodes which are unlikely to exit during task execution. We first quantify the abortion risk of each task-node pair to an expected extra overhead time, and formulate a risk-aware task assignment problem that strives to minimize the average completion time of all tasks, as well as the expected extra overhead time of each task. We then design a novel task assignment scheme to solve this problem with genetic algorithm. Finally, we implement a prototype system to evaluate the performance. The experimental results show that our scheme outperforms the state-of-art task assignment schemes in terms of average completion time and deadline miss rate in most cases.
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This work is supported by National Natural Science Foundation of China (62172155, 62072465).
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Jin, H., Liang, J., Liu, F. (2022). Risk-Aware Optimization of Distribution-Based Resilient Task Assignment in Edge Computing. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13157. Springer, Cham. https://doi.org/10.1007/978-3-030-95391-1_17
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