TL;DR
Sensitivity and ablation studies confirm stable learning and controllable latency-cost trade-offs, demonstrating that lightweight RL can effectively deliver cost-efficient, adaptive autoscaling in hybrid cloud environments.
Reinforcement learning-based adaptive resource management framework is proposed that enables cloud systems to autonomously learn optimal resource allocation policies through continuous interaction with the environment and significantly outperforms static and reactive baseline strategies in terms of resource utilization efficiency and response time stability.
This paper proposes an innovative Deep Reinforcement Learning-based Intelligent Task Scheduling Framework (DRITS) designed to optimize task allocation and resource utilization in cloud distributed systems and establishes DRL-based intelligent scheduling as a promising solution for next-generation cloud computing infrastructure management.
Results indicate that the proposed SBA-DRL approach effectively addresses key challenges in cloud task scheduling, offering a practical solution to enhance the efficiency and sustainability of cloud systems.
Experimental evaluation on a heterogeneous synthetic benchmark demonstrates that the proposed DDQN scheduler reduces SLA violations by approximately 85% relative to Round Robin and 72% relative to the greedy baseline, while achieving superior energy efficiency.
Experimental results consistently validate the effectiveness of H2-LBM in improving latency stability and system efficiency for large-scale LLM inference services.