VAMP: Visual Analytics for Microservices Performance

• URL: http://arxiv.org/abs/2404.14273v1
• Date: Mon, 22 Apr 2024 15:22:56 GMT
• Title: VAMP: Visual Analytics for Microservices Performance
• Authors: Luca Traini, Jessica Leone, Giovanni Stilo, Antinisca Di Marco,
• Abstract summary: Existing distributed tracing tools leverage swimlane as the primary means to support performance analysis. We introduce vamp once, the performance analysis of multiple end-to-end requests. We show how vamp aids in identifying RPC execution time deviations with significant impact on end-to-end performance.
• Score: 2.5824043688763543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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