Multi-level Computation and Power Consumption Models

Abstract

It is critical to reduce the electric power consumed by servers in a cluster in order to realize eco society. In the multi-level power consumption (MLPC) model of a server with a multi-core CPU, the power consumption of the server depends on the number of active cores and active threads where at least one application process is performed. In our previous studies, we discuss the energy-aware (EA) selection algorithm to select a server for each request process. Here, a server which is expected to consume the minimum electric energy is selected in a cluster. A server consumes the basic electric power even if no process is performed. The ratio of the basic energy consumption to the total electric energy consumption is large, e.g. 40 to 50 %. In this paper, we newly propose a globally energy-aware (GEA) algorithm to select a server for each process in a cluster. Here, not only the total electric energy consumption of the servers but also the ratio of basic electric energy consumed by servers to the total energy consumption can be reduced. We evaluate the GEA algorithm and show not only the total energy consumption of the servers but also the average execution time of processes are reduced in the GEA algorithm compared with the EA, round-robin (RR), and random (RD) algorithms.