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Egret: Reinforcement Mechanism for Sequential Computation Offloading in Edge Computing
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DOI：10.1109/TSC.2024.3478826.png)
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摘要
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As an emerging computing paradigm, edge computing offers computational resources closer to the data sources, helping to improve the service quality of many real-time applications. A crucial problem is designing a rational pricing mechanism to maximize the revenue of the edge computing service provider (ECSP). However, prior works have considerable limitations: clients are static and are required to disclose their preferences, which is impractical. To address this issue, we propose a novel sequential computation offloading mechanism, where the ECSP posts prices of computational resources with different configurations to clients in turn. Clients independently choose which computational resources to rent and how to offload based on their prices. Then Egret, a deep reinforcement learning-based approach that achieves maximum revenue, is proposed. Egret determines the optimal price and visiting orders online without infringing on clients' privacy. Experimental results show that the revenue of ECSP in Egret is only 1.29% lower than Oracle and 23.43% better than the state-of-the-art when the client arrives dynamically.
Keyword:
Pricing
Computational modeling
Heuristic algorithms
Costs
Servers
Games
Privacy
Multi-access edge computing
Deep reinforcement learning
Bandwidth
Computation offloading
deep reinforcement learning
edge computing
sequential pricing
期刊
IF:
5.8
论文数:
2.2K
被引数:
6.5K
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