Design and implementation of a low-latency network scheduling algorithm for edge-oriented computing

With the rapid development of the Internet of Things and real-time applications, the demand for low-latency network scheduling by edge computing is becoming increasingly urgent. However, traditional scheduling algorithms face the challenges of high latency and insufficient efficiency in dynamic task allocation, resource heterogeneity, and burst traffic scenarios. This paper designs and implements a low-latency network scheduling algorithm for edge computing. By building a hierarchical scheduling framework, combining dynamic task priority evaluation and resource pre-allocation mechanism, the collaboration efficiency between edge nodes is optimized. The algorithm adopts a distributed task queue management strategy, dynamically adjusts the task distribution weight, and introduces a lightweight feedback mechanism to correct scheduling decisions in real time. Experimental results show that in a simulated edge computing environment, compared with the classical scheduling algorithm, the algorithm in this paper reduces the average latency by 28.7%, the task completion rate increases by 19.4%, and can maintain a stable throughput in high concurrency scenarios. The research verifies the effectiveness of the proposed algorithm in reducing end-to-end latency and improving resource utilization, and provides new solutions for real-time service scheduling in edge computing scenarios.