research-article

GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications

Authors:

Gagan Somashekar,

Tania Lorido Botran,

Anshul GandhiAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3085 - 3095

https://doi.org/10.1145/3589334.3645665

Published: 13 May 2024 Publication History

Abstract

Microservices architecture is quickly replacing monolithic and multi-tier architectures as the implementation choice for large-scale web applications as it allows independent development, scalability, and maintenance. However, even with careful node scheduling and scaling, the microservices applications are still vulnerable to performance degradation due to unexpected (dependent or independent) events like anomalous node behavior, workload interference, or sudden spikes in requests or retries. These events can adversely affect the performance of one or more microservices (bottlenecks), degrading the overall application performance. To ensure a good customer experience and avoid revenue loss, it is crucial to detect and mitigate all bottlenecks swiftly.

This work introduces GAMMA, a novel, explainable graph learning model that integrates a mixture of experts to detect multiple bottlenecks. We evaluated GAMMA using a popular open-source benchmarking application deployed on Kubernetes under various practical bottleneck scenarios. Our experimental evaluation results show that GAMMA provides significantly better performance (46% higher F1 score) than existing works that employ deep learning, machine learning, and statistical techniques, demonstrating its ability to detect multiple bottlenecks by learning complex interactions in a microservices architecture.

The dataset is made publicly available [49] for reproducibility and further research in the field.

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Cited By

Shetty MChen YSomashekar GMa MSimmhan YZhang XMace JVandevoorde DLas-Casas PGupta SNath SBansal CRajmohan S(2024)Building AI Agents for Autonomous Clouds: Challenges and Design PrinciplesProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698525(99-110)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698525
Ding SXu YLu ZTang FLi TGe J(2024)Power Microservices Troubleshooting by Pretrained Language Model with Multi-source Data2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00241(1768-1775)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00241

Index Terms

GAMMA: Graph Neural Network-Based Multi-Bottleneck Localization for Microservices Applications

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Cited By

Shetty MChen YSomashekar GMa MSimmhan YZhang XMace JVandevoorde DLas-Casas PGupta SNath SBansal CRajmohan S(2024)Building AI Agents for Autonomous Clouds: Challenges and Design PrinciplesProceedings of the 2024 ACM Symposium on Cloud Computing10.1145/3698038.3698525(99-110)Online publication date: 20-Nov-2024
https://dl.acm.org/doi/10.1145/3698038.3698525
Ding SXu YLu ZTang FLi TGe J(2024)Power Microservices Troubleshooting by Pretrained Language Model with Multi-source Data2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA)10.1109/ISPA63168.2024.00241(1768-1775)Online publication date: 30-Oct-2024
https://doi.org/10.1109/ISPA63168.2024.00241

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