AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes a deep reinforcement learning-based task scheduling approach (DRLTS) for the fog-cloud environment, which outperforms heuristics, metaheuristics, and reinforcement learning-based scheduling techniques when it comes to response time, energy efficiency, deadline satisfaction, and performance overall.
The rapid growth of Internet of Things (IoT) applications has introduced significant challenges in efficient task scheduling within distributed cloud environments, particularly in meeting Quality of Service (QoS) requirements while minimizing operational costs and SLA violations. To address this issue, this paper propo...
Experimental evaluation under dynamic IoT workloads demonstrates significant reductions in average task latency and response time compared with FCFS, Round Robin, centralized DQN, and conventional federated reinforcement learning schedulers.
An AI-enabled dynamic task scheduling framework based on Deep Reinforcement Learning (DRL) with a Deep Q-Network (DQN) model to dynamically assign tasks to virtual machines and learn the best scheduling policies by continuously interacting with the cloud environment based on system parameters such as resource availabil...
Comparative tests with PPO, FIFO, FAIR and HAS baselines confirm that multi-agent reinforcement learning can well capture the intrinsic scheduling patterns of complex mobile environments, providing an adaptive and energy-efficient scheduling solution for practical IoT deployments.
Robust Reinforcement Learning (RL) based task scheduling approaches can address the inherent tradeoff between energy consumption and deadline violation in a Multi-access Edge Computing (MEC) based Internet of Things (IoT) network, while maintaining robustness against changes in the task arrival rate. However, tabular r...
An adaptive cloud-edge scheduler using lightweight artificial intelligence models for real-time IoT stream placement that improves scheduling flexibility, transparency, and practical applicability in real-time IoT systems is proposed.
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