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This paper investigates the relationship between performance antipatterns and energy consumption in microservice architectures by implementing ten common antipatterns as isolated microservices and measuring their performance, CPU/DRAM power consumption, and resource utilization under controlled load. The study reveals that while all implemented antipatterns degrade performance, only a subset significantly increase power consumption, with some reaching CPU saturation and others exhibiting energy-performance coupling. The findings provide a basis for identifying performance antipatterns that also act as energy antipatterns, offering insights for energy-efficient microservice design.
Performance bottlenecks don't always mean higher energy use: some microservice antipatterns saturate CPU power, while others show a direct link between slowdowns and energy waste.
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.
