Task Offloading Algorithm for Large-scale Multi-access Edge Computing Scenarios

LU Xianling; LI Dekang

doi:10.11999/JEIT240624

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LU Xianling, LI Dekang. Task Offloading Algorithm for Large-scale Multi-access Edge Computing Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240624

Citation:

LU Xianling, LI Dekang. Task Offloading Algorithm for Large-scale Multi-access Edge Computing Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240624

LU Xianling, LI Dekang. Task Offloading Algorithm for Large-scale Multi-access Edge Computing Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240624

Citation:

LU Xianling, LI Dekang. Task Offloading Algorithm for Large-scale Multi-access Edge Computing Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240624

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Task Offloading Algorithm for Large-scale Multi-access Edge Computing Scenarios

doi: 10.11999/JEIT240624

LU Xianling^,,
LI Dekang

School of Internet of Things, Jiangnan University, Wuxi 214122, China

Funds: The National Natural Science Foundation of China (61773181)

Received Date: 2024-07-18
Rev Recd Date: 2024-12-03

Available Online: 2024-12-09

Abstract

Abstract

The task offloading algorithm based on single-agent reinforcement learning encounters strategy degradation issues due to the mutual influence between agents when addressing task offloading in large-scale Multi-access Edge Computing (MEC) systems. In contrast, traditional multi-agent algorithms, such as the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) suffer from poor scalability as the dimensions of the joint action space increase proportionally with the number of agents in the system. To address these issues, the large-scale MEC task offloading problem is modeled as a Partially Observable Markov Decision Process (POMDP), and a task offloading algorithm based on mean-field multi-agent reinforcement learning is proposed. The introduction of a Long Short-Term Memory (LSTM) network addresses the partial observability problem, while mean-field approximation theory reduces the dimensionality of the joint action space. Simulation results demonstrate that the proposed algorithm outperforms single-agent task offloading algorithms in terms of task delay and task drop rate. Furthermore, even with reduced dimensions of the joint action space, the algorithm maintains performance in terms of task delay and task drop rate consistent with MADDPG.
- Multi-access Edge Computing(MEC),
- Task offloading,
- Reinforcement learning,
- Multi-agent algorithms,
- Mean field approximation theory

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References(21)

References

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