Selection Algorithms to Select Energy-Efficient Servers for Storage and Computation Processes

It is now critical to reduce electric energy consumed by servers in a cluster, especially scalable systems like cloud computing systems. In a cluster of servers, most application processes like Web applications use not only CPU but also storages like HDD. In our previous studies, the power consumption MLPCS model and computation MLCMS model are proposed. Ways to energy-efficiently perform processes which use either CPU or storages are discussed in a cluster of servers. In this paper, we consider a more general type of process which does both the computation and accesses to storages. By using the MLPCMS and MLCMS models, we propose LEAS and GEAS algorithms to select servers to perform processes in a cluster so that the total electric energy consumption of the servers can be reduced. We evaluate the algorithms in terms of total electric energy consumption of the servers and average execution time of the processes. We show the electric energy consumed by servers can be reduced in the LEAS and GEAS algorithms.