FNN在分布式环境中的负载均衡研究

Research on Load Balancing of Distributed System Based on Fuzzy Neural Networks

以工作站机群系统 ( NOW— Network of Workstations)为研究对象 ,提出一种基于模糊神经网络 ( FNN)的负载预测和负载均衡策略 .负载均衡是并行计算机系统研究中的关键问题之一 .传统的负载均衡技术 ,如集中策略及阈值策略等 ,总是以当前计算机的实际负载进行任务调度 ,没有考虑负载预测问题 ,从而使计算机的资源消耗较大 ,易造成负载均衡控制滞后的情况 .本文将模糊神经网络引入负载均衡策略 ,利用其处理不确定性问题和自学习能力 ,进行负载预测 ,较好地解决这一问题 .最后通过仿真 。

In this paper, a kind of load prediction and congestion control policy based on FNN (fuzzy neural networks) is proposed for NOW (Networks of Workstation). Load balancing is one of the key problems in NOW. The normal technique of load balancing, such as master-slave scheduler and threshold scheduler, always assign the task based on the present load of workstation. So, the resource utility is low and control of load balancing is lagged. The fuzzy neural network scheme presented in this paper can solve these limitations satisfactorily for its good capability of processing inaccurate information and learning. The results of simulations show that the FNN scheme is effective.