High-Concurrency Distributed Task Scheduling with Fault Tolerance and Load Balancing

Abstract

In modern distributed computing environments, scheduled task scheduling systems play a key role in data synchronization, batch processing, and automated operation and maintenance. However, traditional single-machine task scheduling methods face problems such as single point failure, task concurrency bottlenecks, and scheduling instability, making it difficult to meet high concurrency and high availability business requirements. To address these challenges, this study designed and implemented a highly available distributed scheduled task scheduling system. The system uses multi-machine hot standby and lock contention triggering mechanisms to ensure reliable triggering of tasks in a distributed environment, and combines ZooKeeper for task coordination to avoid duplicate execution problems. In addition, the system optimizes computing resource utilization through dynamic load balancing strategies, and uses asynchronous RPC interactions to improve task scheduling throughput. In order to verify the stability of the system, this study conducted integration tests and high availability tests. Experimental results show that the system can still ensure the normal operation of task scheduling in the case of multi-node failures, improving the reliability of task execution. This study provides an efficient and stable distributed task scheduling solution for enterprise-level applications, which can be widely used in the Internet, finance, telecommunications and other industries, and has important engineering value and theoretical significance.