Energy-Aware Clusters of Servers for Storage and Computation Applications

Abstract

It is now critical to reduce electric energy consumed in a cluster of servers, especially scalable systems like cloud computing systems. In clusters, most application processes like web applications use not only CPU resources but also files and databases. In this paper, we consider storage processes which read and write data in files in addition to computation processes. We propose a PCS model (power consumption model for a storage server) which shows how much electric power a server consumes to perform storage and computation processes. We also propose a CS model (a computation model for storage server) which shows how long it is expected to take to perform storage processes and computation processes. By using the PCS and CS models, we propose a local energy-aware (LEA) algorithm to select a server for a request process in a cluster so that the total electric energy consumption of the servers can be reduced. We evaluate the LEA algorithm in terms of total electric energy consumption of the servers. We show the electric energy consumed by servers to perform computation and storage processes can be reduced in the LEA algorithm.