Using Microservice Telemetry Data for System Dynamic Analysis

TL;DR

This paper explores leveraging telemetry data from microservices to perform dynamic system analysis, aiming to detect quality issues and anti-patterns in decentralized, evolving microservice architectures.

Contribution

It introduces a novel approach combining existing telemetry tools with anomaly detection techniques and presents a prototype for large-scale microservice systems.

Findings

Feasibility of combining telemetry data with anomaly detection.

Effective identification of system quality issues.

Potential for improving microservice system monitoring.

Abstract

Microservices bring various benefits to software systems. They also bring decentralization and lose coupling across self-contained system parts. Since these systems likely evolve in a decentralized manner, they need to be monitored to identify when possibly poorly designed extensions deteriorate the overall system quality. For monolith systems, such tasks have been commonly addressed through static analysis. However, given the decentralization and possible language diversity across microservices, static analysis tools are lacking. On the other hand, there are available tools commonly used by practitioners that offer centralized logging, tracing, and metric collection for microservices. In this paper, we assess the opportunity to combine current dynamic analysis tools with anomaly detection in the form of quality metrics and anti-patterns. We develop a tool prototype that we use to assess a large microservice system benchmark demonstrating the feasibility and potential of such an approach.