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Title: B-MEG: Bottlenecked-Microservices Extraction Using Graph Neural Networks
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 10X increase in recall for detecting bottlenecks compared to the technique used in existing work for bottleneck detection in microservices.  more » « less
Award ID(s):
1750109
PAR ID:
10330226
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the 13th ACM/SPEC International Conference on Performance Engineering (ICPE'22)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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