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A data-driven solution for root cause analysis in cloud computing environments.

Pereira, Rosangela De Fátima

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Escola Politécnica 2016-12-05

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  • Título:
    A data-driven solution for root cause analysis in cloud computing environments.
  • Autor: Pereira, Rosangela De Fátima
  • Orientador: Carvalho, Tereza Cristina Melo de Brito
  • Assuntos: Análise De Causa Raiz; Computação Em Nuvem; Redes Bayesianas; Bayesian Networks; Cloud Computing; Root Cause Analysis
  • Notas: Dissertação (Mestrado)
  • Notas Locais: Programa Engenharia Elétrica
  • Descrição: The failure analysis and resolution in cloud-computing environments are a a highly important issue, being their primary motivation the mitigation of the impact of such failures on applications hosted in these environments. Although there are advances in the case of immediate detection of failures, there is a lack of research in root cause analysis of failures in cloud computing. In this process, failures are tracked to analyze their causal factor. This practice allows cloud operators to act on a more effective process in preventing failures, resulting in the number of recurring failures reduction. Although this practice is commonly performed through human intervention, based on the expertise of professionals, the complexity of cloud-computing environments, coupled with the large volume of data generated from log records generated in these environments and the wide interdependence between system components, has turned manual analysis impractical. Therefore, scalable solutions are needed to automate the root cause analysis process in cloud computing environments, allowing the analysis of large data sets with satisfactory performance. Based on these requirements, this thesis presents a data-driven solution for root cause analysis in cloud-computing environments. The proposed solution includes the required functionalities for the collection, processing and analysis of data, as well as a method based on Bayesian Networks for the automatic identification of root causes. The validation of the proposal is accomplished through a proof of concept using OpenStack, a framework for cloud-computing infrastructure, and Hadoop, a framework for distributed processing of large data volumes. The tests presented satisfactory performance, and the developed model correctly classified the root causes with low rate of false positives.
  • DOI: 10.11606/D.3.2017.tde-03032017-082237
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Escola Politécnica
  • Data de criação/publicação: 2016-12-05
  • Formato: Adobe PDF
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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