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Architecture and Performance Anti-patterns Correlation in Microservice Architectures
Conference proceeding

Architecture and Performance Anti-patterns Correlation in Microservice Architectures

Alberto Avritzer, Andrea Janes, Catia Trubiani, Helena Rodrigues, Yuanfang Cai, Daniel Sadoc Menasche, Alvaro Jose Abreu de Oliveira and IEEE COMPUTER SOC
Proceedings (Working IEEE/IFIP Conference on Software Architecture. Online), pp 60-71
31 Mar 2025
DOI: https://doi.org/10.1109/ICSA65012.2025.00016

Abstract

Antipattern detection Benchmark testing Correlation Couplings Microservice architecture Microservice architectures Object recognition Performance evaluation Root cause analysis Scalability Software architecture Stakeholders Computer Architecture
Microservice architecture design requires the architect to meet the needs of multiple stakeholders and to address their needs for maintainability, scalability, and availability. In the microservice architecture context, a comprehensive performance and scalability assessment is a dynamic activity, which is focused on the detection of service level metric deviations from objectives using a defined operational profile. Root cause analysis is focused on the identification of the activated microservice components given the defined load profile. Therefore, performance issues are identified by detecting dynamic deviations from the expected behaviors of the service level metric.In contrast, microservice architecture assessment focus is on identifying implicit relations among microservice components. Architecture anti-patterns are identified by detecting deviations from the defined formal design patterns. As the ultimate objective of microservice architecture design is to build high-quality applications it would be expected that architecture refactoring based on the removal of architecture anti-patterns will result in meeting stakeholder needs of better scalability and availability.In this paper we present an empirical assessment of architecture anti-pattern detection in combination with the identification of performance issues using two state of the art tools: DV8 for architecture and PPTAM for performance. We make use of Train Ticket, i.e., a benchmark microservice system, and we observed the co-occurrence of architectural (Clique) and performance (Blob) anti-patterns, noting that high coupling shows much worse performance scores. We have found strong correlation between the normalized distance performance metric and architecture coupling values using several similarity metrics. Our empirical results show that operational profile based performance testing and analysis can be used to help prioritize architecture refactoring.

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