Análise de Dependabilidade em Nuvens OpenStack Multi-Nó via Injeção de Falhas Geradas por IA
Resumo
A expansão das nuvens privadas distribuídas torna essencial a avaliação de dependabilidade, sobretudo quanto à propagação de erros entre nós. Este trabalho analisa de forma experimental essa propagação em uma nuvem OpenStack multi-nó. As falhas são injetadas de forma automatizada e geradas por Inteligência Artificial (IA). Cada falha é uma mutação semântica derivada de um bug real. Os resultados indicam que 94,4% das execuções produziram Falhas Cinzentas Distribuídas: os serviços permaneceram ativos no monitoramento, enquanto a infraestrutura ficou funcionalmente incapaz de provisionar instâncias. Esse comportamento revela uma classe de ameaças invisível à observabilidade tradicional.
Referências
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Basiri, A., Behnam, N., de Graaff, R., Hochstein, L., Kosewski, L., Reynolds, J., and Rosenthal, C. (2016). Chaos engineering. IEEE Software, 33(3):35–41.
Cotroneo, D. and Liguori, P. (2024). Neural fault injection: Generating software faults from natural language. In Proceedings of the 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S), pages 23–27.
Cotroneo, D., Simone, L. D., Liguori, P., Natella, R., and Bidokhti, N. (2019). How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform. In Proc. of ESEC/FSE, pages 200–211.
Cotroneo, D., Simone, L. D., and Natella, R. (2022). ThorFI: A novel approach for network fault injection as a service. Journal of Network and Computer Applications, 201:103334.
Duarte, G. S., de Sousa, E. T. G., and e Silva, C. M. N. (2026). Uma análise comparativa entre LLMs proprietários e de pesos abertos na injeção de falhas de nuvens privadas. In Anais do XXVII Workshop de Testes e Tolerância a Falhas (WTF), pages 219–231, Porto Alegre, RS, Brasil. SBC.
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., and Zhang, J. M. (2023). Large language models for software engineering: Survey and open problems. In Proc. of ICSE-FoSE, pages 31–53.
Flexera (2024). 2024 state of the cloud report.
Jia, Y. and Harman, M. (2011). An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering, 37(5):649–678.
Ju, X., Soares, L., Shin, K. G., Ryu, K. D., and Silva, D. D. (2013). On fault resilience of OpenStack. In Proceedings of the 4th Annual Symposium on Cloud Computing (SoCC), pages 2:1–2:16.
Liguori, P., Improta, C., Natella, R., Cukic, B., and Cotroneo, D. (2024). Enhancing AI-based generation of software exploits with contextual information. In Proceedings of the 35th IEEE International Symposium on Software Reliability Engineering (ISSRE), pages 180–191.
Natella, R., Cotroneo, D., and Madeira, H. S. (2013). On fault representativeness of software fault injection. IEEE Transactions on Software Engineering, 39(1):80–96.
Natella, R., Cotroneo, D., and Madeira, H. S. (2016). Assessing dependability with software fault injection: A survey. ACM Computing Surveys, 48(3):1–55.
Ojdanic, M., Barr, E. T., Grunske, L., and Papadakis, M. (2023). Syntactic versus semantic similarity of artificial and real faults in mutation testing studies. IEEE Transactions on Software Engineering, 49(7):3922–3938.
OpenInfra Foundation (2023). 2023 OpenInfra user survey.
Basiri, A., Behnam, N., de Graaff, R., Hochstein, L., Kosewski, L., Reynolds, J., and Rosenthal, C. (2016). Chaos engineering. IEEE Software, 33(3):35–41.
Cotroneo, D. and Liguori, P. (2024). Neural fault injection: Generating software faults from natural language. In Proceedings of the 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S), pages 23–27.
Cotroneo, D., Simone, L. D., Liguori, P., Natella, R., and Bidokhti, N. (2019). How bad can a bug get? an empirical analysis of software failures in the OpenStack cloud computing platform. In Proc. of ESEC/FSE, pages 200–211.
Cotroneo, D., Simone, L. D., and Natella, R. (2022). ThorFI: A novel approach for network fault injection as a service. Journal of Network and Computer Applications, 201:103334.
Duarte, G. S., de Sousa, E. T. G., and e Silva, C. M. N. (2026). Uma análise comparativa entre LLMs proprietários e de pesos abertos na injeção de falhas de nuvens privadas. In Anais do XXVII Workshop de Testes e Tolerância a Falhas (WTF), pages 219–231, Porto Alegre, RS, Brasil. SBC.
Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., and Zhang, J. M. (2023). Large language models for software engineering: Survey and open problems. In Proc. of ICSE-FoSE, pages 31–53.
Flexera (2024). 2024 state of the cloud report.
Jia, Y. and Harman, M. (2011). An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering, 37(5):649–678.
Ju, X., Soares, L., Shin, K. G., Ryu, K. D., and Silva, D. D. (2013). On fault resilience of OpenStack. In Proceedings of the 4th Annual Symposium on Cloud Computing (SoCC), pages 2:1–2:16.
Liguori, P., Improta, C., Natella, R., Cukic, B., and Cotroneo, D. (2024). Enhancing AI-based generation of software exploits with contextual information. In Proceedings of the 35th IEEE International Symposium on Software Reliability Engineering (ISSRE), pages 180–191.
Natella, R., Cotroneo, D., and Madeira, H. S. (2013). On fault representativeness of software fault injection. IEEE Transactions on Software Engineering, 39(1):80–96.
Natella, R., Cotroneo, D., and Madeira, H. S. (2016). Assessing dependability with software fault injection: A survey. ACM Computing Surveys, 48(3):1–55.
Ojdanic, M., Barr, E. T., Grunske, L., and Papadakis, M. (2023). Syntactic versus semantic similarity of artificial and real faults in mutation testing studies. IEEE Transactions on Software Engineering, 49(7):3922–3938.
OpenInfra Foundation (2023). 2023 OpenInfra user survey.
Publicado
08/07/2026
Como Citar
DUARTE, Guilherme Silva; SOUSA, Erica Teixeira Gomes de.
Análise de Dependabilidade em Nuvens OpenStack Multi-Nó via Injeção de Falhas Geradas por IA. In: ESCOLA REGIONAL DE ALTO DESEMPENHO DA REGIÃO NORDESTE (ERAD-NE), 7. , 2026, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 63-67.
DOI: https://doi.org/10.5753/erad-ne.2026.26631.
