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Adaptive two-stage task offloading based on meta reinforcement learning for mobile edge computing
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DOI：10.1007/s11227-025-07274-y.png)
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Abstract
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In mobile edge computing (MEC), the limited resources of individual edge servers and the uneven workload can significantly impact the system performance and user quality of experience (QoE), necessitating greater consideration for edge-edge and edge-cloud collaboration. However, existing collaborative task offloading strategies still fall short in performance and lack adaptability to dynamic environments. To address these challenges, this paper proposes a meta-reinforcement learning-based two-stage task offloading algorithm, named MRL-TSO, for decentralized task offloading in a generic MEC framework that integrates edge-edge and edge-cloud cooperation. MRL-TSO is the first to leverage MRL to address collaborative task offloading for edge-edge and edge-cloud at the edge layer and innovatively divides the offloading decision into two stages: in the first stage, a trained decision model is used to make preliminary decisions based on local information; in the second stage, the initial decision is combined with the status of edge servers to filter out unavailable resources, thereby improving the reliability of the decision. Experimental results indicate that, compared to the state-of-the-art work, MRL-TSO increases the task success rate by at least 1.43% and reduces latency by 9.69%. Especially in the case of edge server failures, it improves the task success rate by at least 45.60% compared to the competitive algorithms, demonstrating better environmental adaptability and robustness.
Keywords:
Mobile edge computing
Decentralized task offloading
Meta reinforcement learning
Deep reinforcement learning
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