基于预测机制的自适应负载均衡算法

工作负载特征对Web服务器集群中负载均衡调度算法的性能有重要影响。针对负载特征在调度算法所起作用的分析和讨论，提出基于预测机制的自适应负载均衡算法（RR_MMMCS-A-P）。通过监测工作负载，预测后续请求到达率和请求大小，快速调整相应参数，实现集群中各服务器之间的负载均衡。实验表明，无论是对计算密集型任务还是数据密集型任务，RR_MMMCS-A-P同基于CPU和CPU-MEM的调度算法相比在缩短平均响应时间方面具有较好的性能。

Adaptive load balancing model based on prediction mechanism

Workload characteristics have an important impact on the performance of load balancing scheduling algorithms in Web server cluster systems. According to the analysis and discussion on the role of load characteristics for scheduling algorithm, a prediction based adaptive load balancing algorithm (RR_MMMCS-A-P) was proposed in this paper. RR_MMMCS-A-P can predict the arrival rate and the size of the follow up request by monitoring the workload characteristics and rapid adjustment of the corresponding parameters in order to balance the load between servers. The experimental results show that compared with CPU based and CPU memory based scheduling algorithm, RR_MMMCS-A-P has better performance in reducing average response time for both calculation intensive and data intensive jobs.