TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
Resource management with deep reinforcement learning
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A survey of ML and DL methods for resource allocation in wireless IoT networks, covering HetNets, MIMO, D2D, and NOMA along with future research directions.
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TIDAL: Recovering Temporal Phase for Cloud Block Storage Placement from LLM-Derived Semantics
TIDAL recovers temporal phase signals from LLM-derived semantics of provisioning metadata to enable complementary CVD placement, reducing overload frequency by 79.1% on production traces.
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Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges
A survey of ML and DL methods for resource allocation in wireless IoT networks, covering HetNets, MIMO, D2D, and NOMA along with future research directions.