Distributed matching theory-based task re-allocating for heterogeneous multi-UAV edge computing

Yangang Wang

doi:10.23919/JCC.fa.2022-0247.202401

Distributed matching theory-based task re-allocating for heterogeneous multi-UAV edge computing

2024-01-01

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AI

DOI：10.23919/JCC.fa.2022-0247.202401

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Many efforts have been devoted to efficient task scheduling in Multi-Unmanned Aerial Vehicle (UAV) edge computing. However, the heterogeneity of UAV computation resource, and the task re-allocating between UAVs have not been fully considered yet. Moreover, most existing works neglect the fact that a task can only be executed on the UAV equipped with its desired service function (SF). In this backdrop, this paper formulates the task scheduling problem as a multi-objective task scheduling problem, which aims at maximizing the task execution success ratio while minimizing the average weighted sum of all tasks' completion time and energy consumption. Optimizing three coupled goals in a real-time manner with the dynamic arrival of tasks hinders us from adopting existing methods, like machine learning-based solutions that require a long training time and tremendous pre-knowledge about the task arrival process, or heuristic-based ones that usually incur along decision-making time. To tackle this problem in a distributed manner, we establish a matching theory framework, in which three conflicting goals are treated as the preferences of tasks, SFs and UAVs. Then, a Distributed Matching Theory-based Re-allocating (DiMaToRe) algorithm is put forward. We formally proved that a stable matching can be achieved by our proposal. Extensive simulation results show that DiMaToRe algorithm outperforms benchmark algorithms under diverse parameter settings and has good robustness.

Keyword:

edge computing

heterogeneity

matching theory

service function

unmanned aerial vehicle

Distributed matching theory-based task re-allocating for heterogeneous multi-UAV edge computing

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