Real-time Monitoring and Performance Optimization Strategy for Large-Scale Distributed Systems

With the widespread application of large-scale distributed systems, real-time monitoring and performance optimization have become crucial. Traditional monitoring methods often face issues of high latency and performance degradation, especially in data collection, transmission, storage, and scheduling. This paper proposes a real-time monitoring and performance optimization framework that integrates eBPF technology, QUIC protocol, deep reinforcement learning, and advanced storage optimization methods. The framework achieves non-intrusive data collection in kernel space through eBPF, effectively reducing system overhead and improving real-time performance; QUIC protocol provides low-latency data transmission, and deep reinforcement learning optimizes load scheduling; LSM-Tree combined with approximate query techniques enhances storage efficiency. Experimental results show that the proposed method outperforms traditional techniques in task completion time, resource utilization, and fault prediction, significantly improving the overall performance and stability of large-scale distributed systems.