research-article

How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform

Authors:

Domenico Cotroneo,

Luigi De Simone,

Pietro Liguori,

Roberto Natella,

Nematollah BidokhtiAuthors Info & Claims

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Pages 200 - 211

https://doi.org/10.1145/3338906.3338916

Published: 12 August 2019 Publication History

Abstract

Cloud management systems provide abstractions and APIs for programmatically configuring cloud infrastructures. Unfortunately, residual software bugs in these systems can potentially lead to high-severity failures, such as prolonged outages and data losses. In this paper, we investigate the impact of failures in the context widespread OpenStack cloud management system, by performing fault injection and by analyzing the impact of the resulting failures in terms of fail-stop behavior, failure detection through logging, and failure propagation across components. The analysis points out that most of the failures are not timely detected and notified; moreover, many of these failures can silently propagate over time and through components of the cloud management system, which call for more thorough run-time checks and fault containment.

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Cited By

Medeiros JSimões MCastelhano JAbreu RCouceiro RHenriques JCastelo-Branco MMadeira HTeixeira Cde Carvalho P(2024)EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging studyPLOS ONE10.1371/journal.pone.029910819:3(e0299108)Online publication date: 7-Mar-2024
https://doi.org/10.1371/journal.pone.0299108
Hassan MSalvador JSantu SRahman A(2024)State Reconciliation Defects in Infrastructure as CodeProceedings of the ACM on Software Engineering10.1145/36607901:FSE(1865-1888)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660790
Yu GChen PHe ZYan QLuo YLi FZheng Z(2024)ChangeRCA: Finding Root Causes from Software Changes in Large Online SystemsProceedings of the ACM on Software Engineering10.1145/36437281:FSE(24-46)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643728
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Index Terms

How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform
1. Computer systems organization
  1. Architectures
    1. Distributed architectures
      1. Cloud computing
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Extra-functional properties
      1. Software fault tolerance
      2. Software reliability

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cover image ACM Conferences

ESEC/FSE 2019: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 2019

1264 pages

ISBN:9781450355728

DOI:10.1145/3338906

General Chairs:
Marlon Dumas
University of Tartu, Estonia
,
Dietmar Pfahl
University of Tartu, Estonia
,
Program Chairs:
Sven Apel
Saarland University, Germany
,
Alessandra Russo
Imperial College, UK

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Published: 12 August 2019

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ESEC/FSE '19: 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

August 26 - 30, 2019

Tallinn, Estonia

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Cited By

Medeiros JSimões MCastelhano JAbreu RCouceiro RHenriques JCastelo-Branco MMadeira HTeixeira Cde Carvalho P(2024)EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging studyPLOS ONE10.1371/journal.pone.029910819:3(e0299108)Online publication date: 7-Mar-2024
https://doi.org/10.1371/journal.pone.0299108
Hassan MSalvador JSantu SRahman A(2024)State Reconciliation Defects in Infrastructure as CodeProceedings of the ACM on Software Engineering10.1145/36607901:FSE(1865-1888)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660790
Yu GChen PHe ZYan QLuo YLi FZheng Z(2024)ChangeRCA: Finding Root Causes from Software Changes in Large Online SystemsProceedings of the ACM on Software Engineering10.1145/36437281:FSE(24-46)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643728
Chen HChen PYu GLi XHe ZZhang H(2024)MicroFI: Non-Intrusive and Prioritized Request-Level Fault Injection for Microservice ApplicationsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2024.3363902(1-18)Online publication date: 2024
https://doi.org/10.1109/TDSC.2024.3363902
Cerveira FFerreira ABarbosa R(2024)Resilient VirtualizationComputer10.1109/MC.2023.330661757:2(70-78)Online publication date: 31-Jan-2024
https://dl.acm.org/doi/10.1109/MC.2023.3306617
Barletta MCinque MDi Martino CKalbarczyk ZIyer R(2024)Mutiny! How Does Kubernetes Fail, and What Can We Do About It?2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)10.1109/DSN58291.2024.00016(1-14)Online publication date: 24-Jun-2024
https://doi.org/10.1109/DSN58291.2024.00016
Cotroneo DLiguori P(2024)Neural Fault Injection: Generating Software Faults from Natural Language2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S)10.1109/DSN-S60304.2024.00016(23-27)Online publication date: 24-Jun-2024
https://doi.org/10.1109/DSN-S60304.2024.00016
Jia KYu XZhang CXie WZhao DXiang J(2024)PMTT: Parallel multi-scale temporal convolution network and transformer for predicting the time to aging failure of software systemsJournal of Systems and Software10.1016/j.jss.2024.112167217(112167)Online publication date: Nov-2024
https://doi.org/10.1016/j.jss.2024.112167
Jia KYu XZhang CXie WZhao DXiang J(2024)TTAFPred: Prediction of time to aging failure for software systems based on a two-stream multi-scale features fusion networkSoftware Quality Journal10.1007/s11219-024-09692-232:4(1481-1513)Online publication date: 22-Jul-2024
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Hadadi FDawes JShin DBianculli DBriand L(2024)Systematic Evaluation of Deep Learning Models for Log-based Failure PredictionEmpirical Software Engineering10.1007/s10664-024-10501-429:5Online publication date: 20-Jun-2024
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