Performance Antipatterns: Angel or Devil for Power Consumption?

TL;DR

This study empirically examines how performance antipatterns in microservices affect energy consumption, revealing that only some antipatterns significantly increase power use while others reach a power cap, influencing energy efficiency.

Contribution

It systematically investigates the relationship between performance antipatterns and power consumption, identifying which antipatterns also serve as energy antipatterns in microservices.

Findings

Some antipatterns cause significant power increases with response time.

Certain antipatterns reach CPU saturation, capping power regardless of response time.

Others show energy-performance coupling, indicating inefficiency.

Abstract

Performance antipatterns are known to degrade the responsiveness of microservice-based systems, but their impact on energy consumption remains largely unexplored. This paper empirically investigates whether widely studied performance antipatterns defined by Smith and Williams also negatively influence power usage. We implement ten antipatterns as isolated microservices and evaluate them under controlled load conditions, collecting synchronized measurements of performance, CPU and DRAM power consumption, and resource utilization across 30 repeated runs per antipattern. The results show that while all antipatterns degrade performance as expected, only a subset exhibit a statistically significant relationship between response time and increased power consumption. Specifically, several antipatterns reach CPU saturation, capping power draw regardless of rising response time, whereas others (\eg Unnecessary Processing, The Ramp) demonstrate energy-performance coupling indicative of inefficiency. Our results show that, while all injected performance antipatterns increase response time as expected, only a subset also behaves as clear energy antipatterns, with several cases reaching a nearly constant CPU power level where additional slowdowns mainly translate into longer execution time rather than higher instantaneous power consumption. The study provides a systematic foundation for identifying performance antipatterns that also behave as energy antipatterns and offers actionable insights for designing more energy-efficient microservices architectures.