VAMP: Visual Analytics for Microservices Performance

TL;DR

VAMP is a visual analytics tool designed to analyze system-wide performance trends in microservices by providing interactive visualizations that help identify performance deviations and structural patterns across multiple requests.

Contribution

It introduces a novel visual analytics approach that extends beyond individual request analysis to system-wide performance trend detection in microservices.

Findings

VAMP effectively identifies RPC execution time deviations impacting performance.

It supports uncovering structural patterns in end-to-end requests.

Demonstrated success on 33 datasets from an open-source microservices system.

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.