Fast energy estimation framework for long-running applications

The computation power in data center facilities is increasing significantly. This brings with it an increase of power consumption in data centers. Techniques such as power budgeting or resource management are used in data centers to increase energy efficiency. These techniques require to know beforehand the energy consumption throughout a full profiling of the applications. This is not feasible in scenarios with long-running applications that have long execution times. To tackle this problem we present a fast energy estimation framework for long-running applications. The framework is able to estimate the dynamic CPU and memory energy of the application without the need to perform a complete execution. For that purpose, we leverage the concept of application signature. The application signature is a reduced version, in terms of execution time, of the original application. Our fast energy estimation framework is validated with a set of long-running applications and obtains RMS values of 11.4% and 12.8% for the CPU and memory energy estimation errors, respectively. We define the concept of Compression Ratio as an indicator of the acceleration of the energy estimation process. Our framework is able to obtain Compression Ratio values in the range of 10.1 to 191.2. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The text discusses a fast energy estimation framework for long-running applications in data center facilities, which uses application signatures to estimate CPU and memory energy consumption without complete execution. The framework achieves low estimation errors and high Compression Ratio values, demonstrating its effectiveness in improving energy efficiency.