Collaborative Task Offloading Optimization for Satellite Mobile Edge Computing Using Multi-Agent Deep Reinforcement Learning

Satellite mobile edge computing (SMEC) achieves efficient processing for space missions by deploying computing servers on low Earth orbit (LEO) satellites, which supplements a strong computing service for future satellite-terrestrial integrated networks. However, considering the spatio-temporal cons...

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Vydané v:IEEE transactions on vehicular technology Ročník 73; číslo 10; s. 15483 - 15498
Hlavní autori: Zhang, Hangyu, Zhao, Hongbo, Liu, Rongke, Kaushik, Aryan, Gao, Xiangqiang, Xu, Shenzhan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Collaboration
Computation offloading
Computational modeling
Cooperative systems
Decisions
Deep learning
Deep reinforcement learning
distributed cooperative computing
Edge computing
Low earth orbit satellites
Low earth orbits
Machine learning
Mobile computing
Multi-access edge computing
multi-agent deep reinforcement learning
Multi-agent systems
Multiagent systems
Networks
Optimization
Redesign
Resource allocation
Resource management
Satellite broadcasting
Satellite communication
Satellite mobile edge computing
Satellite observation
Satellites
Space missions
ISSN:0018-9545, 1939-9359
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Popis
Shrnutí:Satellite mobile edge computing (SMEC) achieves efficient processing for space missions by deploying computing servers on low Earth orbit (LEO) satellites, which supplements a strong computing service for future satellite-terrestrial integrated networks. However, considering the spatio-temporal constraints on large-scale LEO networks, inter-satellite cooperative computing is still challenging. In this paper, a multi-agent collaborative task offloading scheme for distributed SMEC is proposed. Facing the time-varying available satellites and service requirements, each autonomous satellite agent dynamically adjusts offloading decisions and resource allocations based on local observations. Furthermore, for evaluating the behavioral contribution of an agent to task completion, we adopt a deep reinforcement learning algorithm based on counterfactual multi-agent policy gradients (COMA) to optimize the strategy, which enables energy-efficient decisions satisfying the time and resource restrictions of SMEC. An actor-critic (AC) framework is effectively exploited to separately implement centralized training and distributed execution (CTDE) of the algorithm. We also redesign the actor structure by introducing an attention-based bidirectional long short-term memory network (Atten-BiLSTM) to explore the temporal characteristics of LEO networks. The simulation results show that the proposed scheme can effectively enable satellite autonomous collaborative computing in the distributed SMEC environment, and outperforms the benchmark algorithms.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3405642