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A Stable Locality-Aware Task Scheduling Mechanism for Mobile Edge Computing With Workflow Task Offloading
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DOI：10.1109/TSC.2025.3640723.png)
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Offloading a plethora of end workflows to edge servers in mobile edge computing (MEC) systems involves a series of coupled decision-making steps, including how much edge resources will be allocated for each workflow, which subtasks will be offloaded, and how to determine edge-end transaction prices to ensure system stability. Particularly, these decisions must jointly account for workflow characteristics, the resources available at each edge server, as well as local constraints (e.g., communication distance, task latency, and bandwidth conditions), which again increases the difficulty of optimizing the problem. However, no existing study addresses such joint optimization problems for these tightly coupled decisions. To fill this gap, a minimum-delay workflow partitioning algorithm is first designed to determine the optimal task offloading solution under various resource conditions. Based on this algorithm, two locality-based social welfare maximization models (basic and dynamic) are constructed. Specifically, for basic model, a multi-stage task matching game with the second lowest cost strategy is developed to determine the resource selection and pricing. For the dynamic model with uncertain requests, an online learning algorithm is introduced to track the dynamic valuations of mobile devices and to ensure that the resulting task allocation solution achieves an upper-bounded regret. Strict theoretical analysis demonstrates that our mechanism guarantees individual rationality, Nash Equilibrium, and stable approximation ratio. Simulation results verify the effectiveness and efficiency of our mechanism, and show that the proposed mechanisms obtain at most 18% higher social welfare than existing studies.
Keyword:
Mobile edge computing
resource allocation
task offloading
game theory
Nash equilibrium
期刊
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5.8
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2.1K
被引数:
6.5K
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