B-MEG: Bottlenecked-Microservices Extraction Using Graph Neural Networks
B-MEG: Bottlenecked-Microservices Extraction Using Graph Neural Networks
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DOI：
10.1145/3491204.3527494
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Gagan Somashekar;Anurag Dutt;R. Vaddavalli;Sai Bhargav Varanasi;Anshul Gandhi
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文献类型：
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作者：
Gagan Somashekar;Anurag Dutt;R. Vaddavalli;Sai Bhargav Varanasi;Anshul Gandhi
微服务体系结构通过其细粒度和模块化设计实现了应用程序组件的独立开发和维护。这使得快速采用微服务架构来构建延迟敏感型在线应用程序成为可能。在这类在线应用程序中，检测和缓解性能下降(瓶颈)的根源至关重要。然而，微服务体系结构的模块化设计导致了一个巨大的交互微服务图，它们之间的相互影响不是微不足道的。在这项前期工作中，我们探索了图神经网络模型在检测瓶颈方面的有效性。初步分析表明，我们的框架B-MEG产生了有希望的结果，特别是对于具有复杂调用图的应用程序。与现有的微服务瓶颈检测技术相比，B-MEG在准确率和精确度上分别提高了15%和14%，在检测瓶颈方面的召回率提高了近10倍。
The microservices architecture enables independent development and maintenance of application components through its fine-grained and modular design. This has enabled rapid adoption of microservices architecture to build latency-sensitive online applications. In such online applications, it is critical to detect and mitigate sources of performance degradation (bottlenecks). However, the modular design of microservices architecture leads to a large graph of interacting microservices whose influence on each other is non-trivial. In this preliminary work, we explore the effectiveness of Graph Neural Network models in detecting bottlenecks. Preliminary analysis shows that our framework, B-MEG, produces promising results, especially for applications with complex call graphs. B-MEG shows up to 15% and 14% improvements in accuracy and precision, respectively, and close to 10× increase in recall for detecting bottlenecks compared to the technique used in existing work for bottleneck detection in microservices.