用强化学习动态分配云任务，提升响应速度和资源利用率。
Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments
- 用强化学习实时分析流量与资源，动态调整任务分配。
- 相比传统算法，响应时间更短，资源利用更均衡。
- 适合需要自适应负载的云服务场景，如弹性计算平台。
在云计算环境中，高效的负载均衡对确保最优资源利用率、降低响应时间并防止服务器过载至关重要。传统的负载均衡算法（如轮询或最少连接）通常为静态策略，难以适应云工作负载的动态变化。本文提出一种基于强化学习（RL）的新型自适应负载均衡框架，通过持续观察系统实时性能，根据流量模式和资源可用性动态优化任务分配。该框架能有效减少延迟，并实现服务器间资源使用的均衡。实验结果表明，所提出的基于RL的负载均衡器在响应时间、资源利用率和对动态工作负载的适应性方面均优于传统算法。这些发现凸显了人工智能驱动方案在提升云基础设施效率与可扩展性方面的潜力。
原文摘要 · Abstract (English)
Efficient load balancing is crucial in cloud computing environments to ensure optimal resource utilization, minimize response times, and prevent server overload. Traditional load balancing algorithms, such as round-robin or least connections, are often static and unable to adapt to the dynamic and fluctuating nature of cloud workloads. In this paper, we propose a novel adaptive load balancing framework using Reinforcement Learning (RL) to address these challenges. The RL-based approach continuously learns and improves the distribution of tasks by observing real-time system performance and making decisions based on traffic patterns and resource availability. Our framework is designed to dynamically reallocate tasks to minimize latency and ensure balanced resource usage across servers. Experimental results show that the proposed RL-based load balancer outperforms traditional algorithms in terms of response time, resource utilization, and adaptability to changing workloads. These findings highlight the potential of AI-driven solutions for enhancing the efficiency and scalability of cloud infrastructures.
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