Abstract
One of the challenges in the Computing Continuum paradigm is the optimal distribution of the generated tasks between the devices in each layer (cloud-fog-edge). In this paper, we propose to use Reinforcement Learning (RL) to solve the Task Assignment Problem (TAP) at the edge layer and then we propose a novel multi-layer extension of RL (ML-RL) techniques that allows edge agents to query an upper-level agent with more knowledge to improve the performance in complex and uncertain situations. We first formulate the task assignment process considering the trade-off between energy consumption and execution time. We then present a greedy solution as a baseline and implement our two RL proposals in the PureEdgeSim simulator. Finally, several simulations of each algorithm are evaluated with different numbers of devices to verify scalability. The simulation results show that reinforcement learning solutions outperformed the heuristic-based solutions and our multi-layer approach can significantly improve performance in high device density scenarios.
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Acknowledgments
This work was supported by the FPI Grant 21463/FPI/20 of the Seneca Foundation in Region of Murcia (Spain), partially funded by project PID2020–112675RB–C44 and PTAS–20211009 MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”.
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Robles-Enciso, A., Skarmeta, A.F. (2022). Task Offloading in Computing Continuum Using Collaborative Reinforcement Learning. In: González-Vidal, A., Mohamed Abdelgawad, A., Sabir, E., Ziegler, S., Ladid, L. (eds) Internet of Things. GIoTS 2022. Lecture Notes in Computer Science, vol 13533. Springer, Cham. https://doi.org/10.1007/978-3-031-20936-9_7
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