大规模分布式系统下的智能化资源监控和负载平衡

Intelligent resource monitoring and load balancing in large-scale distributed systems

随着云计算和物联网技术的蓬勃发展,大规模分布式系统应用得到了广泛的关注。然而,更为复杂的资源监控和负载平衡问题随之而来。对此,文章提出了一系列智能化策略,旨在改善系统的性能和稳定性。其中,首先采用了基于机器学习的资源使用预测方法以了解系统的资源需求;其次引入了自适应负载平衡算法,在合理分配各节点任务的同时降低了系统延迟;最后设计了一种大规模系统的智能监控方案,以实时掌握系统状态。实验证明,这些策略在提高资源使用效率、缩短响应时间以及提升系统整体性能方面具有显著作用。

With the vigorous development of cloud computing and Internet of Things technology,large-scale distributed system applications have received widespread attention.However,more complex resource monitoring and load balancing issues arise.This article proposes a series of intelligent strategies aimed at improving the performance and stability of the system.Firstly,a machine learning based resource usage prediction method was adopted to understand the system's resource requirements.Secondly,an adaptive load balancing algorithm was introduced to reduce system latency while reasonably allocating tasks to each node.Finally,an intelligent monitoring scheme for large-scale systems was designed to grasp the system status in real-time.Experiments have shown that these strategies have a significant effect on improving resource utilization efficiency,shortening response time,and enhancing overall system performance.