This paper proposes a resource on-demand approach for Web applications, which can efficiently online reconfigure clusters in response to time-varying resource requirements. It can also dynamically decide the number of running nodes and virtual machines deployed on them. It first predicts the future workloads of the applications with Brown's quadratic exponential smoothing method to make reconfiguration catch up with demands. Next, it adopts a genetic algorithm to parallel find the optimal reconfiguration policy. Experimental results demonstrate the approach can online adapt the cluster resource according to the change of requirement, increase the cluster resource utilization and greatly reduce power consumption.%面向Web应用,提出一种动态资源按需配置方法,能够根据不断变化的资源需求以在线方式高效地重配置集群,实时地确定集群当前节点运行数量及其上部署的虚拟机类型.该方法基于布尔二次指数平滑法预测用户请求,有效避免了配置结果落后于资源请求；基于遗传算法并行化搜索配置空间,快速发现合理配置.实验结果表明,该方法能够根据需求变化高效地在线调整系统资源配置,并可有效提高集群资源利用率,显著降低了系统能耗.
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