VAMP: Visual Analytics for Microservices Performance
<|reference_start|>VAMP: Visual Analytics for Microservices Performance: Analysis of microservices' performance is a considerably challenging task due to the multifaceted nature of these systems. Each request to a microservices system might raise several Remote Procedure Calls (RPCs) to services deployed on different servers and/or containers. Existing distributed tracing tools leverage swimlane visualizations as the primary means to support performance analysis of microservices. These visualizations are particularly effective when it is needed to investigate individual end-to-end requests' performance behaviors. Still, they are substantially limited when more complex analyses are required, as when understanding the system-wide performance trends is needed. To overcome this limitation, we introduce vamp, an innovative visual analytics tool that enables, at once, the performance analysis of multiple end-to-end requests of a microservices system. Vamp was built around the idea that having a wide set of interactive visualizations facilitates the analyses of the recurrent characteristics of requests and their relation w.r.t. the end-to-end performance behavior. Through an evaluation of 33 datasets from an established open-source microservices system, we demonstrate how vamp aids in identifying RPC execution time deviations with significant impact on end-to-end performance. Additionally, we show that vamp can support in pinpointing meaningful structural patterns in end-to-end requests and their relationship with microservice performance behaviors.<|reference_end|>
@article{traini2024vamp:,
    title={VAMP: Visual Analytics for Microservices Performance},
    author={Luca Traini, Jessica Leone, Giovanni Stilo, Antinisca Di Marco},
    journal={VAMP: Visual Analytics for Microservices Performance. In The 39th
  ACM/SIGAPP Symposium on Applied Computing (SAC '24), April 8-12, 2024, Avila,
  Spain},
    year={2024},
    doi={10.1145/3605098.3636069},
    archivePrefix={arXiv},
    eprint={2404.14273},
    primaryClass={cs.SE}
}