METRION: A Framework for Accurate Software Energy Measurement

TL;DR

METRION is a comprehensive framework that accurately measures application-level energy consumption on CPUs and DRAM, aiding energy optimization efforts in ICT by providing precise attribution at the thread level across platforms.

Contribution

The paper introduces an extensible, cross-platform energy attribution model integrated into METRION, capable of accurately quantifying CPU and DRAM energy use at the thread level.

Findings

CPU energy attribution with 4.2% MAPE

DRAM energy attribution with 16.1% MAPE

Effective across different workloads and architectures

Abstract

The Information and Communication Technology sector accounted for approximately 1.4% of global greenhouse gas emissions and 4% of the world's electricity consumption in 2020, with both expected to rise. To reduce this environmental impact, optimization strategies are employed to reduce energy consumption at the IT infrastructure and application levels. However, effective optimization requires, firstly, the identification of major energy consumers and, secondly, the ability to quantify whether an optimization has achieved the intended energy savings. Accurate determination of application-level energy consumption is thus essential. Therefore, we introduce an energy attribution model that quantifies the energy consumption of applications on CPU and DRAM at the thread level, considering the influence of Simultaneous Multithreading, frequency scaling, multi-socket architectures, and Non-Uniform Memory Access. To ensure cross-platform applicability, we integrate the proposed model into an extensible framework, METRION, including a platform-independent data model and an initial implementation for Linux systems using Intel CPUs. We evaluate METRION across three different workloads and demonstrate that the energy attribution model can accurately capture the CPU energy consumption of applications targeting solely the CPU with a Mean Absolute Percentage Error of 4.2%, and the DRAM energy consumption of applications targeting DRAM with an 16.1% error.