The article examines the issues of monitoring performance in microservice architectures. We explore the problem of forecasting performance indicators as well as fault propagation in such systems, which are distributed and have independent service deployments. The paper addresses these issues by proposing a novel approach that uses multimetric time series data to establish causal relationships between microservices and build graph neural networks based on revealed system dependencies. The method’s goal is to proactively forecast performance indicators and fault propagation in order to assure the resilience and reliability of microservices. Various graph neural network architectures are discussed. The best one DCRNN uses a diffusion convolutional recurrent neural network in a basis and is able to predict well both on data with and without anomalies
Original languageEnglish
Title of host publicationComputational Science and Its Applications – ICCSA 2024 Workshops
Pages343–357
Number of pages15
DOIs
StatePublished - 22 Aug 2024
EventThe 24th International Conference on Computational Science and Its Applications, ICCSA 2024 - Ханой, Viet Nam
Duration: 1 Jul 20244 Jul 2024
https://2024.iccsa.org/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature
Volume14821
ISSN (Print)0302-9743

Conference

ConferenceThe 24th International Conference on Computational Science and Its Applications, ICCSA 2024
Abbreviated titleICCSA
Country/TerritoryViet Nam
CityХаной
Period1/07/244/07/24
Internet address

    Research areas

  • Graph neural networks, Metrics prediction, Microservice, microservice preformance

ID: 123266494