Predictively booting nodes to minimize performance degradation of a power-aware web cluster

With the ever increasing trend of dynamic and static content web, clusters have been widely used for large-scale web servers to improve the system scalability. Dynamically switching the cluster nodes between different power states is one effective approach to save the energy in such clusters. Many research efforts have been invested in designing power-aware clusters by using this method. However, booting a cluster node from a low-power state to an active state takes a certain amount of time that depends on different configurations. This process incurs significant performance degradation. The existing work normally trades a certain amount of performance degradation for energy saving. This paper proposes a hybrid method to predict the number of requests per booting time of the web workloads. A power-aware web cluster scheduler is designed to divide the cluster nodes into an active group and a low-power group. The scheduler attempts to minimize the active group and maximize the low-power group, and boot the cluster nodes in the low-power group in advance to minimize/eliminate performance degradation by leveraging the prediction scheme. Furthermore, this paper integrates the power awareness into the conventional load balancers including Least Connections, Deficit Round Robin, and Skew. Comprehensive experiments are performed to explore the potential opportunities to minimize/eliminate the performance degradation of the power-aware web cluster.