基于蚁群算法的网络负载均衡的研究与实现

随着3G网络的日益普及和云网络集群与分布式并行计算的广泛应用,对网络速度和服务质量提出了更高要求。蚁群算法作为一种生物仿生式的启发算法,通过模拟蚁群间信息素的相互协作与动态更新,实时对网络流量负载进行均衡,并对路由选择进行优化。研究结果和仿真实验表明,该算法在解决网络拥塞瓶颈和集群分布式并行计算中,具有较好的研究价值与发展前景。

Research and implementation of network load balance based on ant colony optimization

As the day-by-day popularization of 3G network and the widespread application of cloud network clusters and distributed parallel computing, it is calling for higher demand on the network speed and quality of service. Ant colony optimization as a bio-inspired algorithm, which can be applied to real-time balance network traffic load and to optimize route choice by simulating the mutual cooperation of ant colony between the pheromone and dynamic updates. The study and simulation results both indicate that ACO used for solving bottlenecks of network congestion and parallel computing of distributed clusters, has better research values and prospects.