Analyzing the Evolution of Structural Communities within Microservice Architecture
For practitioners and researchers analyzing microservice degradation, this work demonstrates a temporal community detection approach to assess architectural evolution, but it is an incremental application of existing methods to a single benchmark.
The authors applied temporal community detection to six releases of a microservice benchmark and found a stable architecture with two communities aligned to business processes, identifying services with multiple community memberships and mixed connection patterns.
In recent years, the detection of anti-patterns in microservice architecture has gained traction, particularly to identify instances of Microservice Architectural Degradation. In such tasks, the microservice architecture is often modeled as a network of microservice dependencies. Recent works have explored how to assess the evolution of such architectural networks by considering the architecture of consecutive releases of the project. Particular anti-patterns related to the structure of the service network include Wrong cuts and Knot services. Community detection is a way to identify groups of services in a network that strongly depend on each other. If such groups cannot be mapped to business processes in the system, or if the same service belongs to multiple communities, this could indicate architectural degradation due to an inappropriate division of responsibilities or unoptimized communication. Temporal community detection methods have been proposed to analyze community structure that evolves in time. We performed temporal community detection within the microservice architecture of six releases of the train-ticket benchmark and analyzed the composition of the discovered communities and their activities over time. We observed a stable architecture with a clear separation of services into two communities, which we could identify with two business processes performed by the system. We found services belonging to several communities, as well as services within the same community with both incoming and outgoing connections. The membership strength metric provided by the leveraged algorithm enables fine-grained assessment of the microservice communities.