Optimal Latency and Energy-Aware Task Scheduling in In-Network Computing Paradigm: A Deep Reinforcement Learning Approach

To support the escalating traffic demands in the 6 G era, the novel computing paradigm of in-network computing (INC), where tasks can be processed on the forwarding path, is emerging with enhanced network performance and improved service quality. Considering the large network scale with high dynamic...

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Veröffentlicht in:	IEEE transactions on sustainable computing 2024-12, p.1-15
Hauptverfasser:	Gao, Jing, Zhou, Fanqin, Dong, Mianxiong, Feng, Lei, Yu, Peng, Ota, Kaoru, Qiu, Xuesong
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational modeling Deep reinforcement learning Delays Dynamic scheduling Energy consumption Heuristic algorithms in -network computing (INC) Job shop scheduling Load modeling neural bellman ford network Optimization Processor scheduling Resource management task scheduling
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