September 11, 2024Future InternetOpen Access

A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing

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Authors

JGJiang GuiwenGuangxi Agricultural Machinery Research Institute RHRongxi HuangGuangxi University ZBZhiming BaoTianjin University

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Implication

Computational experiment demonstrates reduced service costs and task drop rates in mobile edge computing, highlighting superior efficiency under heavy network workloads.

Key Points

Decentralized multi-agent reinforcement learning reduces average service costs by 19.4% to 66.6% in mobile edge computing, while accelerating overall convergence speed.
Modeling of cloud-edge collaborative computing applies an actor-critic framework and proximal policy optimization, integrating a Transformer neural network for task offloading.
Supports resilient resource allocation in cloud-edge networks, maintaining a critical task drop rate of 5.5% under high computational load and severe server failure rates.

Cite This Study

Guiwen et al. (2024) studied this question.

synapsesocial.com/papers/68e58cc8b6db64358752880d https://doi.org/10.3390/fi16090333

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September 11, 2024Future InternetOpen Access

A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing

View Full Paper

Ask AI

Bookmark

Authors

JGJiang GuiwenGuangxi Agricultural Machinery Research Institute RHRongxi HuangGuangxi University ZBZhiming BaoTianjin University

Discussion

Member takes

Implication

Computational experiment demonstrates reduced service costs and task drop rates in mobile edge computing, highlighting superior efficiency under heavy network workloads.

Key Points

Decentralized multi-agent reinforcement learning reduces average service costs by 19.4% to 66.6% in mobile edge computing, while accelerating overall convergence speed.
Modeling of cloud-edge collaborative computing applies an actor-critic framework and proximal policy optimization, integrating a Transformer neural network for task offloading.
Supports resilient resource allocation in cloud-edge networks, maintaining a critical task drop rate of 5.5% under high computational load and severe server failure rates.

Cite This Study

Guiwen et al. (2024) studied this question.

synapsesocial.com/papers/68e58cc8b6db64358752880d https://doi.org/10.3390/fi16090333

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