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Towards Optimal Configuration of Microservices

Authors:

Gagan Somashekar,

Anshul GandhiAuthors Info & Claims

EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems

Pages 7 - 14

https://doi.org/10.1145/3437984.3458828

Published: 26 April 2021 Publication History

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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Cited By

Roshan Mahant Sumit Bhatnagar Vikas Mendhe (2024)Optimizing Service Placement and Enhancing Service Allocation for Microservice Architectures in Cloud EnvironmentsInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-18276(493-505)Online publication date: 16-May-2024
https://doi.org/10.48175/IJARSCT-18276
Yang TChen RLi YLiu XWang G(2023)CoTuner: A Hierarchical Learning Framework for Coordinately Optimizing Resource Partitioning and Parameter TuningProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605578(317-326)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605578
Akgun IAydin ABurford AMcNeill MArkhangelskiy MZadok E(2023)Improving Storage Systems Using Machine LearningACM Transactions on Storage10.1145/356842919:1(1-30)Online publication date: 19-Jan-2023
https://dl.acm.org/doi/10.1145/3568429
Show More Cited By

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cover image ACM Conferences

EuroMLSys '21: Proceedings of the 1st Workshop on Machine Learning and Systems

April 2021

130 pages

ISBN:9781450382984

DOI:10.1145/3437984

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGOPS: ACM Special Interest Group on Operating Systems

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Published: 26 April 2021

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NSF (National Science Foundation)

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EuroSys '21

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SIGOPS

EuroSys '21: Sixteenth European Conference on Computer Systems

April 26, 2021

Online, United Kingdom

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EuroMLSys '21 Paper Acceptance Rate 18 of 26 submissions, 69%;

Overall Acceptance Rate 18 of 26 submissions, 69%

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EuroSys '25

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Twentieth European Conference on Computer Systems

March 30 - April 3, 2025

Rotterdam , Netherlands

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Cited By

Roshan Mahant Sumit Bhatnagar Vikas Mendhe (2024)Optimizing Service Placement and Enhancing Service Allocation for Microservice Architectures in Cloud EnvironmentsInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-18276(493-505)Online publication date: 16-May-2024
https://doi.org/10.48175/IJARSCT-18276
Yang TChen RLi YLiu XWang G(2023)CoTuner: A Hierarchical Learning Framework for Coordinately Optimizing Resource Partitioning and Parameter TuningProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605578(317-326)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605578
Akgun IAydin ABurford AMcNeill MArkhangelskiy MZadok E(2023)Improving Storage Systems Using Machine LearningACM Transactions on Storage10.1145/356842919:1(1-30)Online publication date: 19-Jan-2023
https://dl.acm.org/doi/10.1145/3568429
Sahu SShukla ATiwari RRao TMaram BMusnansyah A(2023)A study on Microservices and Application Programming Interface vulnerabilities2023 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS)10.1109/ICADEIS58666.2023.10271023(1-6)Online publication date: 2-Aug-2023
https://doi.org/10.1109/ICADEIS58666.2023.10271023
Zhang DWu QZhao YLi ZHe PZhang YDuan XZhang YMiao GTong JXie G(2023)MALT: Fine-Grained Microservice Profiling for Request Latency Anomaly Localization2023 IEEE International Conference on High Performance Computing & Communications, Data Science & Systems, Smart City & Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys)10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00025(114-121)Online publication date: 17-Dec-2023
https://doi.org/10.1109/HPCC-DSS-SmartCity-DependSys60770.2023.00025
Somashekar G(2023)Towards Performance Management of Large-Scale Microservices Applications2023 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOS-C58168.2023.00028(24-26)Online publication date: 25-Sep-2023
https://doi.org/10.1109/ACSOS-C58168.2023.00028
Li YLee B(2022)Phronesis: Efficient Performance Modeling for High-dimensional Configuration TuningACM Transactions on Architecture and Code Optimization10.1145/354686819:4(1-26)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.1145/3546868
Bushong VAbdelfattah AMaruf ADas DLehman AJaroszewski ECoffey MCerny TFrajtak KTisnovsky PBures M(2021)On Microservice Analysis and Architecture Evolution: A Systematic Mapping StudyApplied Sciences10.3390/app1117785611:17(7856)Online publication date: 26-Aug-2021
https://doi.org/10.3390/app11177856
Dinh-Tuan HKatsarou KHerbke P(2021)Optimizing microservices with hyperparameter optimization2021 17th International Conference on Mobility, Sensing and Networking (MSN)10.1109/MSN53354.2021.00105(685-686)Online publication date: Dec-2021
https://doi.org/10.1109/MSN53354.2021.00105

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