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Creators/Authors contains: "Somashekar, Gagan"

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  1. ; ; ; ; ( , ACM)
    Free, publicly-accessible full text available May 13, 2025
  2. ; ; ; ; ; ; ; ; ( , 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24))
    Free, publicly-accessible full text available April 19, 2025
  3. ; ; ( , International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS))
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  4. ; ; ; ; ( , Proceedings of the 13th ACM/SPEC International Conference on Performance Engineering (ICPE'22))
    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. 
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  5. ; ( , Proceedings of the 1st Workshop on Machine Learning and Systems)
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  6. ; ; ; ; ; ( , 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems)
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  7. ; ; ( , ACM SIGMETRICS Performance Evaluation Review)
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