Design and Evaluation of Algorithms to Energy-Efficiently Select Servers for Storage and Computation Processes

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 but also storages like databases. In this paper, we consider storage processes which read and write data in files in addition to computation processes. We propose an MLPCMS (power consumption model for a storage server) model which shows how much electric power a server consumes to perform storage and computation processes. We also propose an MLCMS (computation model for a storage server) model which shows the expected execution time of storage and computation processes. By using the MLPCMS and MLCMS models, we propose a GEAS (globally energy-aware server with storage processes) algorithm to select servers to perform computation and storage processes in a cluster so that the total electric energy consumption of the servers can be reduced. We evaluate the GEAS algorithm in terms of total electric energy consumption of the servers. We show the electric energy consumed by servers can be reduced in the GEAS algorithm.