Our master’s student, Marijn Vollaard, has achieved a significant milestone by completing and presenting his literature study titled “Hardware Dimensioning for Microservice Applications in Cyber-Physical Systems: Current Directions and Challenges” The study addresses the challenge of dimensioning the number of compute nodes required to meet the performance demands of microservice-based applications in cyber-physical systems. It thoroughly reviews an extensive body of literature on application and system profiling, performance prediction, and design-space exploration to establish the current state of knowledge in this field. The survey culminates in a discussion about how the surveyed literature applies to microservice applications, the cyber-physical systems context, and the problem of hardware dimensioning. Overall, this is a nice piece of work with a lot of references presented in a systematic way. Congratulations to Marijn for his great effort!”
Master Thesis Tackles Architectural Anti-patterns in Microservice Applications
Today, we are delighted to announce the successful defense of the outstanding master’s thesis titled “Architectural Anti-Pattern Identification and Mitigation in Microservice Applications Based on Telemetry” by our master student, Amund Lunke Rohne from the University of Amsterdam. This master’s project was a collaborative effort involving TNO-ESI and Thales.
The thesis addresses the problem that microservices offer benefits like scalability and separation of concerns, but also introduce many complex service dependencies. The decomposition of microservice applications can impact system performance and maintainability and can lead to architectural anti-patterns over time. While simple anti-patterns can be detected using analysis of service dependencies, there is a lack of formal mathematical definitions which prevents more complex anti-patterns from being automatically detected by tools. The thesis introduces a novel model called Granular Hardware Utilization-Based Service Dependency Graph (GHUBS), a fine-grained model that captures the interactions dependencies between services at the level of individual requests. The GHUBS model can be manually specified in early design phases to validate a microservice decomposition, or automatically created using telemetry data from a running application. Mathematical formalizations are introduced for four common architectural anti-patterns and methods for automatically detecting them using the GHUBS model is presented. A method for recommending how to mitigate the identified anti-patterns based on the service dependencies in the GHUBS model, as well as resource utilization metrics for the services, is also presented. The approach is implemented in a tool called Televisor and validated through case studies on open-source microservice benchmarking applications, revealing instances of these anti-patterns.
We thank Amund for his work and a fruitful collaboration, and wish him the best of luck in his future career.
3rd Annual MasCot Program Day 2023 Focuses on Technology Transfer and Industry-Academia Collaboration
