Abstract
The microservice architecture allows applications to be designed in a modular format, whereby each microservice can implement a single functionality and can be independently managed and deployed. However, an undesirable side-effect of this modular design is the large state space of possibly inter-dependent configuration parameters (of the constituent microservices) which have to be tuned to improve application performance. This workshop paper investigates optimization techniques and dimensionality reduction strategies for tuning microservices applications, empirically demonstrating the significant tail latency improvements (as much as 23%) that can be achieved with configuration tuning.
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CITATION STYLE
Somashekar, G., & Gandhi, A. (2021). Towards Optimal Configuration of Microservices. In Proceedings of the 1st Workshop on Machine Learning and Systems, EuroMLSys 2021 (pp. 7–14). Association for Computing Machinery, Inc. https://doi.org/10.1145/3437984.3458828
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