B-MEG

Gagan Somashekar, Anurag Dutt, Rohith Vaddavalli, Sai Bhargav Varanasi, Anshul Gandhi
2022 Companion of the 2022 ACM/SPEC International Conference on Performance Engineering  
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
more » ... ce 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 [32] . CCS CONCEPTS • Computer systems organization → Reliability; • Software and its engineering → Software maintenance tools.
doi:10.1145/3491204.3527494 fatcat:67s7ltoq4rgvpbkbabji4doxmq