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Modeling Performance of Microservices Systems with Growth Theory
Matteo Camilli1, Barbara Russo1
1Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano, Italy.
Empirical Software Engineering
|January 17, 2022
Summary
This study uses growth theory to model microservice performance violations, identifying resilient services through S-shaped models and non-resilient ones via linear models. The approach helps predict and manage microservice resilience.
Area of Science:
- Computer Science
- Software Engineering
- Performance Engineering
Background:
- Microservices architecture is increasingly popular but doesn't guarantee system performance.
- Understanding and predicting performance requirement violations is crucial for system resilience.
- Techniques like load balancing and scaling need data-driven insights for effective tuning.
Purpose of the Study:
- To analyze microservice performance requirement violations using time series analysis.
- To develop tools for detecting resilient and non-resilient microservices.
- To predict the future performance behavior of microservices.
Main Methods:
- Introduced a novel method based on growth theory to model performance violation occurrences as a stochastic process.
- Applied the method to both in-vitro e-commerce benchmarks and in-production telecommunication systems.
- Interpreted growth models to characterize microservices based on their transient performance behavior.
Main Results:
- Non-linear S-shaped growth models accurately capture violations in resilient microservices, indicating bounded performance degradation.
- Linear models identified non-resilient microservices exhibiting constant growth and unbounded performance violations.
- Real-world system analysis revealed additional resilience profiles showing varied service reactions and violation occurrence patterns.
Conclusions:
- Growth theory effectively models performance violations in both simulated and real-world microservice systems.
- The model calibration heuristics are computationally inexpensive.
- The derived models offer insights into performance violation trends, aiding engineers in identifying and addressing problematic microservice operations.
Keywords:
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