A predictive adaptive load balancing model

Performance of load balancing scheduling policies in Web server cluster systems is greatly impacted by the characteristics of workload. Based on the analysis of the load characteristics for scheduling algorithm, a prediction-based adaptive load balancing model (RR_MMMCS-A-P) is proposed in this paper. Monitoring the workload characteristics and its variation, the arrival rate and the size of the follow-up request are predicted by RR_MMMCS-A-P and rapid adjustment of the corresponding parameters to balance the load between servers. Experiments have shown that compared with CPU-based and CPU-memory based scheduling strategy, RR_MMMCS-A-P have better performance in reducing average response time for both calculation-intensive and data-intensive jobs.