Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption

TL;DR

This paper tackles the rising energy footprint of data-center software by introducing a formal energy debugging methodology that localizes energy hotspots in configurable software, demonstrated on Redis running atop Alpine and Ubuntu. The approach combines precise energy measurements, program tracing, and log-alignment to pinpoint causative components, with memcpy in the musl libc identified as a key energy hotspot behind Alpine’s higher energy consumption. The work provides a replication package and a systematic workflow that can guide developers in tracing energy regressions and evaluating library choices, contributing to sustainable software engineering practices. The findings underscore the importance of considering energy efficiency in software dependencies and base images, especially at scale, where small per-instance differences accumulate significantly.

Abstract

Energy consumption in software systems is becoming increasingly important, especially in large-scale deployments. However, debugging energy-related issues remains challenging due to the lack of specialized tools. This paper presents an energy debugging methodology for identifying and isolating energy consumption hotspots in software systems. We demonstrate the methodology's effectiveness through a case study of Redis, a popular in-memory database. Our analysis reveals significant energy consumption differences between Alpine and Ubuntu distributions, with Alpine consuming up to 20.2% more power in certain operations. We trace this difference to the implementation of the memcpy function in different C standard libraries (musl vs. glibc). By isolating and benchmarking memcpy, we confirm it as the primary cause of the energy discrepancy. Our findings highlight the importance of considering energy efficiency in software dependencies and demonstrate the capability to assist developers in identifying and addressing energy-related issues. This work contributes to the growing field of sustainable software engineering by providing a systematic approach to energy debugging and using it to unveil unexpected energy behaviors in Alpine.

Figures (11)

• Figure 1: Visual representation of the energy debugging methodology
• Figure 2: Energy Consumption of PostgreSQL on Alpine vs. Ubuntu.
• Figure 3: uftrace report of top used functions.
• Figure 4: Example of a checkpoint line in PostgreSQL logs.
• Figure 5: Evolution of the function usage over time with the vertical red lines as the identified checkpoints in the logs.