Energy-Aware Server Selection Algorithms in a Scalable Cluster

It is critical to reduce the electric energy consumed in information systems, especially server clusters. In this paper, we extend the multi-level power consumption (MLPC) model and the multi-level computation (MLC) model to a server with multiple CPUs. In this paper, we newly propose a totally energy-aware (TEA) algorithm to select a server for a process in a cluster. Here, servers in a cluster are first classified into subclusters. Each subcluster is characterized in terms of the electric power and computation rate. One server is randomly selected in each subcluster. Then, one server is selected so that the expected electric energy is minimum in the selected servers. We evaluate the TEA algorithm and show not only the total electric energy consumption of the servers but also the average execution time of processes are reduced in the TEA algorithm compared with other algorithms.