Abstract
Fog computing offers a flexible solution for computational offloading for Internet of Things (IoT) services at the edge of wireless networks. It serves as a complement to traditional cloud computing, which is not cost-efficient for most offloaded tasks in IoT applications involving small-to-medium levels of computing tasks. Given the heterogeneity of tasks and resources in fog computing, it is vital to offload each task to an appropriate destination to fully utilize the potential benefit of this promising technology. In this paper, we propose a scalable priority-based index policy, referred to as the Prioritized Incremental Energy Rate (PIER), to optimize the energy efficiency of the network. We demonstrate that PIER is asymptotically optimal in a special case applicable for local areas with high volumes of homogeneous offloaded tasks and exponentially distributed task durations. In more general cases with statistically different offloaded tasks, we further demonstrate the improvement of PIER over benchmark policies in terms of energy efficiency and the robustness of PIER to different task duration distributions by extensive simulations. Our results show that PIER can perform better than benchmark policies in more than \(78.6\%\) of all simulation runs.
The work described in this paper is supported by Student Interdisciplinary Research Fund from BNU-HKBU United International College, College Research Grant from BNU-HKBU United International College R201911, and Zhuhai Basic and Applied Basic Research Foundation Grant ZH22017003200018PWC.
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Notes
- 1.
Orthogonal wireless channel allocations eliminate intra-cell interference and utilize frequency spectrum resources more efficiently.
- 2.
While the more general cases with non-negligible activation delay and power consumption can be addressed by integrating the vacation queuing model with activation cost and delay as in [14], they will complicate the analysis and we do not consider them in this paper due to the limited space.
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Yin, J., Fu, J., Wu, J., Zheng, S. (2022). Energy Efficient Priority-Based Task Scheduling for Computation Offloading in Fog 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 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_35
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