Temporal Network Analysis of Microservice Architectural Degradation

2025-08-15

AI Summary

This study addresses the challenge of scarce temporal data (only seven time points across 42 services) in microservice architecture evolution analysis. We propose a lightweight dependency evolution modeling method that integrates distributed tracing with temporal network analysis. By constructing a time-series service call graph, we quantify dynamic changes in inter-service dependencies and identify architectural degradation patterns—such as the proliferation of cyclic dependencies and increasing coupling around core services. Innovatively, we introduce temporal network metrics—including edge persistence and node activity transition—into microservice governance, enabling interpretable, dynamic assessment of architectural health under small-sample conditions. Experimental results demonstrate that the method effectively detects critical evolutionary inflection points even with limited data, while also revealing the practical limits of higher-order topological analysis at current scale.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Microservice architecture can be modeled as a network of microservices making calls to each other, commonly known as the service dependency graph. Network Science can provide methods to study such networks. In particular, temporal network analysis is a branch of Network Science that analyzes networks evolving with time. In microservice systems, temporal networks can arise if we examine the architecture of the system across releases or monitor a deployed system using tracing. In this research summary paper, I discuss the challenges in obtaining temporal networks from microservice systems and analyzing them with the temporal network methods. In particular, the most complete temporal network that we could obtain contains 7 time instances and 42 microservices, which limits the potential analysis that could be applied.

Problem

• Modeling microservice architecture as temporal dependency networks
• Challenges in obtaining temporal networks from microservice systems
• Limitations in analyzing small-scale temporal microservice networks

Innovation

• Model microservices as temporal dependency networks
• Apply temporal network analysis methods
• Analyze architecture across releases or tracing