SLA aware deep reinforcement learning for adaptive EdgeCloud task scheduling

Nature·Feb 20

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Research has been conducted on using deep reinforcement learning for adaptive task scheduling in edge-cloud computing, with a focus on efficiency and optimization. Various approaches have been explored, including hybrid frameworks, bi-level mobility-aware methods, and negotiation-augmented federated reinforcement learning. The goal of these efforts appears to be improving the energy efficiency, cost effectiveness, and reliability of task scheduling in cloud and edge computing environments.

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