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为了提高web集群负载均衡的效果,结合web服务用户访问静动态内容的特征,提出了一种基于访问特征负载预测的负载均衡算法。首先建立网络带宽负载和CPU、内存综合性能负载的小波包-支持向量机回归混合预测模型;然后根据用户请求的类型,结合负载预测的结果对任务进行分配和调度。仿真结果表明:与传统的基于负载预测的负载均衡算法相比,基于访问特征负载预测的负载均衡算法能达到更好的负载均衡效果,从而有效提高web集群的整体性能。
In order to improve the effect of web cluster load balancing, combined with the characteristics of web service users accessing static and dynamic content, a load balancing algorithm based on access feature load prediction is proposed. Firstly, a wavelet packet-SVM regression model with network bandwidth load, CPU and memory performance load is established. Then, the task is allocated and scheduled according to the type of user request and the result of load prediction. Simulation results show that, compared with the traditional load balancing algorithm based on load forecasting, the load balancing algorithm based on access signature load forecasting can achieve a better load balancing effect and thus improve the overall performance of the web cluster effectively.