AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
TADOF is a dynamic optimization framework that jointly performs energy-aware elastic scaling and task migration under drift-aware periodic modeling and burst detection and observed about 18–34% lower energy and 15–28% lower migration cost than threshold and A2C/TD3 baselines while preserving QoS.
The results show that soft SLO limits reduce corrective rescheduling actions by 49% compared to hard-limit approaches while maintaining acceptable performance guarantees, and resource-aware scheduling decreases node-level congestion and further mitigates SLO violations, demonstrating the effectiveness of incorporating...
The lack of determinism restricts the integration of safety-critical applications into Edge–Fog–Cloud (EFC) architectures. Existing EFC schedulers are typically designed for dynamic, best-effort operation based on unmanaged resource allocation and elastic virtualization. This paradigm introduces unbounded queueing, res...
Energy-aware task scheduling in heterogeneous cloud infrastructures remains challenging due to the combinatorial growth of task-to-resource assignments, resource heterogeneity, and the need to balance energy consumption with scheduling performance. This paper proposes an Adaptive Dominance-Guided Grey Wolf Optimizer (A...
A hybrid congestion-aware autoscaling framework for serverless platforms inspired by TCP slow-start and congestion control principles is proposed, which significantly improves latency performance, scalability, and resource efficiency in serverless environments.
A Risk-Aware Task Scheduling Strategy (RATSS) that incorporates variability into the optimization process by minimizing the standard deviation of makespan and waiting time, thereby reducing execution uncertainty and demonstrating improved stability, fairness, and overall system performance.
Cloud computing has transformed the delivery of modern applications and services by providing scalable, flexible, and cost-effective access to computing resources. One of the most critical challenges in cloud environments is the efficient distribution of dynamic workloads across heterogeneous resources, commonly addres...
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