MINERVA: Performance Prediction of Microservice Applications with Black-box Container Orchestration

• Applicants: Dr. André Bauer; Professor Dr.-Ing. Samuel Kounev
• Subject Area: Software Engineering and Programming Languages
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 510552229

Modern distributed systems are nowadays typically based on microservice architectures, which are considered a best practice among software developers. Modern microservice applications are commonly deployed in containers managed by a container orchestration framework (e.g., Kubernetes), which provides many self-adaptation features such as autoscaling, intelligent load balancing, circuit breakers, and failure recovery. While such features significantly influence the application performance, the internal mechanisms they use are normally considered a black box from the perspective of application developers. This makes it challenging to understand, quantify, compare, and optimize the performance properties of microservice applications and orchestration frameworks. Questions such as the following arise: How should the adaptation mechanisms be configured to optimize the tradeoff between performance and operating costs? How long would the system take to adapt to a sudden load spike of three times the current load? What would be the expected time-to-recovery and performance degradation if a certain service instance or a node fails? Existing performance prediction approaches can only be used to evaluate the steady-state performance and they normally require the explicit modeling of possible adaptations and adaptation rules. This is infeasible due to the large number of potential adaptations and the fact that many adaptation mechanisms rely on complex machine learning models. With some approaches, the adaptation logic itself may evolve during operation. To the best of our knowledge, there is no model-based performance prediction approach that considers both steady-state and transient phases, while not requiring the explicit modeling of the adaptation logic. The goal of the project is to develop such an approach for microservice applications with black-box container orchestration. It should enable application developers and system operators to answer performance-related questions like the above. The project will target three main goals: (1) modeling formalisms to capture the system aspects relevant for predicting both transient and steady-state performance of modern microservice applications in environments with black-box container orchestration, (2) efficient and scalable algorithms for simulating the performance of a system modeled using the proposed modeling formalisms, and (3) novel analysis algorithms and workflows to analyze and interpret the results obtained through the developed modeling and simulation approach. Further, we will design and implement a novel benchmark as well as metrics for comparing adaptation mechanisms in a standardized manner based on the proposed simulation framework. A series of case studies will be conducted to benchmark different adaptation mechanisms and to individually validate the methods, models, and tools developed under the project as well as to validate the overall proposed approach and its end-to-end performance.

• DFG Programme: Research Grants