Abstract
Fog Computing (FC) is an extension of cloud computing, providing services closer to the users near the edge. In FC, the Mobile Devices (MDs) can offload heavy tasks to an edge or cloud server. The decision of whether a MD offloads tasks to a Mobile Edge Computing (MEC) or a Mobile Cloud Computing (MCC) servers should be carefully studied. In this paper, an Advanced Deep Reinforcement Learning (ADRL) protocol is proposed to improve task offloading. It can generate multi-class offload decisions for executing independent tasks locally, at the edge, or cloud while considering the current workload of MD, MEC, and MCC to maintain balance in the mobile system. The experiments show the efficiency of the proposed algorithm in terms of the computation time and convergence to the optimal solution.
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Hashem, W., Attia, R., Nashaat, H., Rizk, R. (2022). Advanced Deep Reinforcement Learning Protocol to Improve Task Offloading for Edge and Cloud Computing. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_51
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