@inproceedings{1799eba7931048009cd23d7809f39b9c,
title = "Towards Optimal Configuration of Microservices",
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.",
keywords = "ML for systems, configuration tuning, microservices, optimization, tail latency",
author = "Gagan Somashekar and Anshul Gandhi",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 1st Workshop on Machine Learning and Systems, EuroMLSys 2021, held in conjunction with ACM EuroSys 2021 ; Conference date: 26-04-2021 Through 26-04-2021",
year = "2021",
month = apr,
day = "26",
doi = "10.1145/3437984.3458828",
language = "English",
series = "Proceedings of the 1st Workshop on Machine Learning and Systems, EuroMLSys 2021",
publisher = "Association for Computing Machinery, Inc",
pages = "7--14",
booktitle = "Proceedings of the 1st Workshop on Machine Learning and Systems, EuroMLSys 2021",
}