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Distributed Microservice Deployment for Satellite Edge Computing Networks: A Multi-Agent Deep Reinforcement Learning Approach
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DOI：10.1109/TVT.2025.3565270.png)
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Abstract
En 中文
Low earth orbit (LEO) satellite networks are promising to carry out edge computing and reduce the service latency in future 6 G networks. Meanwhile, the microservice architecture provides a lightweight and flexible approach for network function deployment in satellite edge computing networks by splitting complex applications into multiple microservices. In satellite edge computing networks, a brand new problem is how to deploy various types of microservices with service function chain (SFC) constraints onto appropriate satellites to efficiently utilize limited on-board resources and reduce the service latency. In this paper, a distributed microservice deployment strategy in satellite edge computing networks is proposed by taking into account time-varying satellite network topology and diverse satellite resources. Specifically, we formulate the deployment problem as a partially observable Markov decision process (POMDP) to minimize the load imbalance as well as the service latency. A distributed multi-agent reinforcement learning (DMARL) scheme is proposed for learning a dynamic deployment strategy. Furthermore, to accommodate more microservices in SFC, a novel attention-based actor-critic strategy is designed to significantly accelerate the convergence of network training. Simulation results demonstrate that the proposed DMARL scheme can achieve a superior performance.
Keywords:
Satellites
Microservice architectures
Edge computing
Vehicle dynamics
Training
Low earth orbit satellites
Heuristic algorithms
Computational modeling
Network topology
Service function chaining
Satellite edge computing
microservice deployment
service function chain
multi-agent deep reinforcement learning
Journal
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Citations:
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