AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The ER-RL-DVFS framework combines an energy-risk task-priority score, DVFS-aware cluster–PM–VM profiling, hierarchical candidate filtering, deterministic score-based assignment for clearly separated candidates, selective tabular reinforcement learning as the prescribed refinement mechanism for near-tie decisions, and migration-aware correction.
In the Open Radio Access Network (O-RAN) 7.2x split, the Distributed Unit (O-DU) must finish upper physicallayer processing before strict fronthaul deadlines. However, heterogeneous acceleration through CPU fallback, shared acceleration, and dedicated acceleration introduces profile-selection, contention, and queueing...
Internet of Medical Things (IoMT) systems supporting remote surgery, ward monitoring, emergency alert systems, and wearable sensors increasingly rely on ultra-reliable low-latency communication (URLLC) transmission. This targeted review systematically summarizes existing research on deep reinforcement learning (DRL)-ba...
Future sixth-generation (6G) radio access networks require schedulers that balance throughput, spectral efficiency, fairness, delay, packet loss, and quality-of-service (QoS) satisfaction. This study presents an audited and reproducible benchmark of a QoS-aware Proximal Policy Optimization (QoS-PPO) scheduler against P...
The rapid growth of multimedia streaming poses critical challenges, including bursty traffic and congestion, leading to playback delays. The existing separate prediction and control mechanisms for multimedia traffic scheduling, which are based on software-defined networks (SDN), are unable to proactively manage bursty...
Cloud computing has emerged as a new paradigm, which entrusts task scheduling to ensure the satisfaction of stringent constraints on latency, energy, and resources for sustainably running real-time applications. State-of-the-art natural DRL-based scheduling solutions mainly rely heavily on DRL techniques and are either...
The results indicate that task-aware objective adaptation and chip-level control can reduce wasteful offloading and thermal stress while preserving service timeliness.
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