Energy consumption is a growing issue in data centers, impacting their economic viability and their public image. In this work we empirically characterize the power and energy consumed by different types of servers. In particular, in order to understand the behavior of their energy and power consumption, we perform measurements in different servers. In each of them, we exhaustively measure the power consumed by the CPU, the disk, and the network interface under different configurations, identifying the optimal operational levels. One interesting conclusion of our study is that the curve that defines the minimal CPU power as a function of the load is neither linear nor purely convex as has been previously assumed. Moreover, we find that the efficiency of the various server components can be maximized by tuning the CPU frequency and the number of active cores as a function of the system and network load, while the block size of I/O operations should be always maximized by applications. We also show how to estimate the energy consumed by an application as a function of some simple parameters, like the CPU load, and the disk and network activity. We validate the proposed approach by accurately estimating the energy of a map-reduce computation in a Hadoop platform.
Arjona et al. REF isolate the power from CPU, disks and network, and propose a power and energy characterization of different type of servers.
A measurement-based analysis of the energy consumption of data center servers
e-Energy '14
Computer Science