AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A resource-aware multi-application operator placement method that optimizes both end-to-end latency and network usage, while meeting QoS constraints and application owners’ preferences in heterogeneous cloud-edge environments is proposed.
A lightweight, QoS-aware service placement algorithm that evaluates latency, bandwidth, and node load in real time is introduced that yields reduced latency and more consistent wait times relative to heuristic and genetic baselines.
The study concludes that intelligent edge computing architectures will play a vital role in supporting future real-time applications and next-generation 6G-enabled digital ecosystems.
The rapid growth of Internet-of-Things (IoT) devices has increased the need for computing support close to end users, particularly for applications that cannot tolerate long processing delays or excessive energy consumption. Fog computing has emerged as a practical extension of the cloud to address these requirements,...
Effective emergency management demands realtime processing and fusion of massive, heterogeneous data streams from IoT sensors, video surveillance, social media, and wireless platforms distributed across disaster-affected regions. Existing cloud-centric architectures suffer from prohibitive latency, while static edge-cl...
In the tested replays, the Long Short-Term Memory (LSTM)-assisted configuration shows lower video and sensor delay with 38–48% lower mean per-flow video throughput than the baseline—a configuration-level latency-versus-throughput trade-off; the LSTM-specific effect is not isolated.
The article presents the Average‐Based Load Balancing and Resource Allocation Mechanism (ALBRAM), which identifies suitable nodes and dynamically allocates resources to maintain the load balance among all the servers to provide fairness, optimal resource utilisation, and balanced workloads.
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