Previsão de falhas em ambientes de nuvem: uma revisão sistemática de literatura

José Carlos dos Santos

ORCID iD Universidade Federal Rural de Pernambuco (UFRPE) Brasil

Tiago Buarque Assunção de Carvalho

ORCID iD Universidade Federal do Agreste de Pernambuco (UFAPE) Brasil

Erica Teixeira Gomes de Sousa

ORCID iD Universidade Federal Rural de Pernambuco (UFRPE) Brasil

Resumo

Atualmente, os serviços de computação em nuvem são fundamentais para a economia moderna. Diversos setores têm reorientado seus serviços para a nuvem. No entanto, a complexidade e natureza distribuída desses sistemas contribuem para a ocorrência de diversos tipos de falhas que, quando não tratadas, podem resultar em danos catastróficos. Para mitigar os efeitos das falhas, diversos métodos de previsão de falhas foram propostos. Como esses métodos mudam com o passar do tempo, esse trabalho tem como objetivo apresentar uma revisão sistemática da literatura a respeito das técnicas e métodos utilizados na previsão de falhas em nuvem. Nesse sentido, 40 artigos de quatro bases de dados cientificamente reconhecidas foram analisados. Ficou evidenciada a predominância de técnicas de aprendizagem de máquina baseadas em árvore de decisão, assim como a escassez de conjuntos de dados do mundo real para o avanço das pesquisas de previsão de falhas em nuvem. Esforços de pesquisa futuros podem avaliar métodos para agregar as amostras de dados de trabalhos e tarefas no conjunto de dados, bem como técnicas para balanceamento de classes em modelos de previsão de falhas em nuvem. Essa revisão sistemática da literatura apresenta o estado atual das pesquisas relacionadas à previsão de falhas nos sistemas em nuvem. As observações e tendências identificadas neste trabalho podem ser úteis para futuros pesquisadores.

Palavras-chave


aprendizagem de máquina; computação em nuvem; previsão de falhas; revisão sistemática


Texto completo:

Referências


ABRO, J. H.; LI, C.; SHAFIQ, M.; VISHNUKUMAR, A.; MEWADA, S.; MALPANI, K.; OSEI-OWUSU, J. Artificial intelligence enabled effective fault prediction techniques in cloud computing environment for improving resource optimization. Scientific Programming, 2022. Disponível em: doi.org/10.1155/2022/7432949. Acessado em: 05 de março de 2023.

ABRO, J. H.; LI, C.; SHAFIQ, M.; VISHNUKUMAR, A.; MEWADA, S.; MALPANI, K.; OSEI-OWUSU, J. Artificial intelligence enabled effective fault prediction techniques in cloud computing environment for improving resource optimization. Scientific Programming, 7432949, 2022. DOI: https://doi.org/10.1155/2022/7432949.

ADAMU, H.; MOHAMMED, B.; MAINA, A. B.; CULLEN, A.; UGAIL, H.; AWAN, I. An approach to failure prediction in a cloud based environment. In: 2017 IEEE INTERNATIONAL CONFERENCE ON FUTURE INTERNET OF THINGS AND CLOUD (FICLOUD), 5., 2017, Prague. Proceedings [...]. Prague: IEEE, 2017. DOI: https://doi.org/10.1109/FiCloud.2017.56.

ADEGBOYEGA, A. Time-series models for cloud workload prediction: a comparison. In: 2017 IFIP/IEEE SYMPOSIUM ON INTEGRATED NETWORK AND SERVICE MANAGEMENT (IM), 2017, Lisbon. Proceedings [...]. Lisbon: IEEE, 2017. DOI: https://doi.org/10.23919/INM.2017.7987292.

ALAHMAD, Y.; DARADKEH, T.; AGARWAL, A. Proactive failure-aware task scheduling framework for cloud computing. IEEE Access, v. 9, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3101147.

AMVROSIADIS, G.; PARK, J. W.; GANGER, G. R.; GIBSON, G. A.; BASEMAN, E.; DEBARDELEBEN, N. On the diversity of cluster workloads and its impact on research results. In: 2018 USENIX ANNUAL TECHNICAL CONFERENCE (USENIX ATC 18), 2018, Boston. Proceedings [...]. Boston: USENIX, 2018. Disponível em: https://www.usenix.org/conference/atc18/presentation/amvrosiadis. Acessado em: 10 de julho de 2023.

ASMAWI, T. N. T.; ISMAIL, A.; SHEN, J. Cloud failure prediction based on traditional machine learning and deep learning. Journal of Cloud Computing, 11, 47, 2022. DOI: https://doi.org/10.1186/s13677-022-00327-0.

BHANAGE, D. A.; PAWAR, A. V.; KOTECHA, K. IT infrastructure anomaly detection and failure handling: a systematic literature review focusing on datasets, log preprocessing, machine deep learning approaches and automated tool. IEEE Access, v. 9, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3128283.

BHATTACHARYYA, A.; SINGH, H.; JANDEGHI, S. A. J.; AMZA, C. Online detection of anomalous applications on the cloud. In: ANNUAL INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING, 27., 2017. Markhan. Proceedings [...]. Markhan: ACM, p. 161-169, 2017. Disponível em: https://dl.acm.org/doi/10.5555/3172795.3172814. Acessado em: 08 de maio de 2023.

BUI, K. T.; VO, L. V.; NGUYEN, C. M.; PHAM, T. V.; TRAN, H. C. A fault detection and diagnosis approach for multi-tier application in cloud computing. Journal of Communications and Networks, v. 22, n. 5, p. 399-414, 2020. DOI: https://doi.org/10.1109/JCN.2020.000023.

BUYYA, R.; SRIRAMA, S. N.; CASALE, G.; CALHEIROS, R.; SIMMHAN, Y.; VARGHESE, B.; GELENBE, E.; JAVADI, B.; VAQUERO, L. M.; NETTO, M. A. S.; TOOSI, A. N.; RODRIGUEZ, M. A.; LLORENTE, I. M.; VIMERCATI, S. D. C. D.; SAMARATI, P.; MILOJICIC, D.; VARELA, C.; BAHSOON, R.; ASSUNCAO, M. D. D.; RANA, O.; ZHOU, W.; JIN, H.; GENTZSCH, W.; ZOMAYA, A. Y.; SHEN, H. A manifesto for future generation cloud computing: research directions for the next decade. ACM Computing Surveys, v. 51, n. 5, 105, p. 1-38, 2018. DOI: https://doi.org/10.1145/3241737.

CHAKRABORTTII, C.; LITZ, H. Improving the accuracy, adaptability, and interpretability of SSD failure prediction models. In: ACM Symposium on Cloud Computing (SoCC 20), 11., 2020, Virtual Event. Proceedings […]. Virtual Event: ACM, p. 120-133, 2020. DOI: https://doi.org/10.1145/3419111.3421300.

CHEN, Y.; YANG, X.; LIN, Q.; ZHANG, H.; GAO, F.; XU, Z.; DANG, Y.; ZHANG, D.; DONG, H.; XU, Y.; LI, H.; KANG, Y. Outage prediction and diagnosis for cloud service systems. In: The World Wide Web Conference (WWW 19), 2019, San Francisco. Proceedings […]. San Francisco: ACM, p. 2659-2665, 2019. DOI: https://doi.org/10.1145/3308558.3313501.

CHHETRI, T. R.; DEHURY, C. K.; LIND, A.; SRIRAMA, S. N.; FENSEL, A. A combined system metrics approach to cloud service reliability using artificial intelligence. Big Data and Cognitive Computing, v. 6, n. 1, 26, 2022. DOI: https://doi.org/10.3390/bdcc6010026.

DAS, A.; MUELLER, F.; ROUNTREE, B. Aarohi: making real-time node failure prediction feasible. In: INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS)., 2020, New Orleans. Proceedings […]. New Orleans: IEEE, 2020. DOI: https://doi.org/10.1109/IPDPS47924.2020.00115.

DIAS, A. H. T.; CORREIA, L. H. A.; MALHEIROS, N. A systematic literature review on virtual machine consolidation. ACM Computing Surveys, v. 54, n. 8, p. 1-38, 2021. DOI: https://doi.org/10.1145/3470972.

FAHIM, M.; SILLITTI, A. Anomaly detection, analysis and prediction techniques in IoT environment: a systematic literature review. IEEE Access, v. 7, p. 81664-81681, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2921912.

GAO, J.; WANG, H.; SHEN, H. Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing, v. 15, n. 3, p. 1411-1422, 2020. DOI: https://doi.org/10.1109/TSC.2020.2993728.

GOKHROO, M. K.; GOVIL, M. C.; PILLI, E. S. Detecting and mitigating faults in cloud computing environment. In: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE COMMUNICATION TECHNOLOGY (CICT), 3., 2017, Ghaziabad. Proceedings […]. Ghaziabad: IEEE, 2017. DOI: https://doi.org/10.1109/CIACT.2017.7977362.

GOLLAPALLI, M.; ALMETRIK, M. A.; ALNAJRANI, B. S.; ALOMARI, A. A.; ALDAWOUD, S. H.; ALMUNSOUR, Y. Z.; ABDULQADER, M. M.; ALOUP, K. M. Task failure prediction using machine learning techniques in the google cluster trace cloud computing environment. Mathematical Modelling of Engineering Problems, v. 9, n. 2, p. 545-553, 2022. DOI: https://doi.org/10.18280/mmep.090234.

GUO, J.; CHANG, Z.; WANG, S.; DING, H.; FENG, Y.; MAO, L.; BAO, Y. Who limits the resource efficiency of my datacenter: an analysis of Alibaba datacenter traces. In: INTERNATIONAL SYMPOSIUM ON QUALITY OF SERVICE (IWQoS 19), 2019, Phoenix. Proceedings […]. Phoenix: ACM, 2019. Disponível em: https://doi.org/10.1145/3326285.3329074.

GUPTA, S.; MUTHIYAN, N.; KUMAR, S.; NIGAM, A.; DINESH, D. A. A supervised deep learning framework for proactive anomaly detection in cloud workloads. In: 2017 INDIA COUNCIL INTERNATIONAL CONFERENCE (INDICON), 14., 2017, Roorkee. Proceedings […].Roorkee: IEEE, 2017. DOI: https://doi.org/10.1109/INDICON.2017.8488109.

HERBST, N.; BAUER, A.; KOUNEV, S.; OIKONOMOU, G.; EYK, E. V.; KOUSIOURIS, G.; EVANGELINOU, A.; KREBS, R.; BRECHT, T.; ABAD, C. L.; IOSUP, A. Quantifying cloud performance and dependability: Taxonomy, metric design, and emerging challenges. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, v. 4, n. 4, p. 1-36, 2018. DOI: https://doi.org/10.1145/3236332.

HERMIAS, J. P.; TEKNOMO, K.; MONJE, J. C. N. Short-term stochastic load forecasting using autoregressive integrated moving average models and hidden Markov model. In: 2017 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES (ICICT), 2017, Karachi. Proceedings […]. Karachi: IEEE, 2017. DOI: https://doi.org/10.1109/ICICT.2017.8320177.

IRRERA, I. Fault injection for online failure prediction assessment and improvement. 2015. Tese (Doutorado em Ciências e Tecnologias da Informação) – Universidade de Coimbra, Coimbra, 2015. Disponível em: https://estudogeral.sib.uc.pt/handle/10316/29182. Acesso em: 14 dez. 2023.

ISLAM, T.; MANIVANNAN, D. Predicting application failure in cloud: a machine learning approach. In: 2017 IEEE INTERNATIONAL CONFERENCE ON COGNITIVE COMPUTING (ICCC), 2017, Honolulu. Proceedings […]. Honolulu: IEEE, 2017. DOI: https://doi.org/10.1109/IEEE.ICCC.2017.11.

JANIESCH, C.; ZSCHECH, P.; HEINRICH, K. Machine learning and deep learning. Electronic Markets, v. 31, p. 685-695, 2021. DOI: https://doi.org/10.1007/s12525-021-00475-2.

JASSAS, M. S.; MAHMOUD, Q. H. Evaluation of a failure prediction model for large scale cloud applications. In: GOUTTE, C.; ZHU, X. (eds.). Advances in Artificial Intelligence. Canadian AI 2020. Lecture Notes in Computer Science, v. 12109. Cham: Springer, 2020. DOI: https://doi.org/10.1007/978-3-030-47358-7_32.

JASSAS, M. S.; MAHMOUD, Q. H. A failure prediction model for large scale cloud applications using deep learning. In: 2021 IEEE INTERNATIONAL SYSTEMS CONFERENCE (SysCon), 2021, Vancouver. Proceedings […]. Vancouver: IEEE, 2021. DOI: https://doi.org/10.1109/SysCon48628.2021.9447141.

JASSAS, M. S.; MAHMOUD, Q. H. Analysis of job failure and prediction model for cloud computing using machine learning. Sensors, v. 22, n. 5, 2025, 2022. DOI: https://doi.org/10.3390/s22052035.

JAUK, D.; YANG, D.; SCHULZ, M. Predicting faults in high performance computing systems: An in-depth survey of the state-of-the-practice. In: International Conference for High Performance Computing, Networking, Storage and Analysis (SC 19), 2019, Denver. Proceedings […]. Denver: ACM, 2019. DOI: https://doi.org/10.1145/3295500.3356185.

KABIR, H. D.; KHOSRAVI, A.; MONDAL, S. K.; RAHMAN, M.; NAHAVANDI, S.; BUYYA, R. Uncertainty-aware decisions in cloud computing: foundations and future directions. ACM Computing Surveys, v. 54, n. 4, p. 1-30, 2021. DOI: https://doi.org/10.1145/3447583.

KASHANI, M. H.; MAHDIPOUR, E. Load balancing algorithms in fog computing: A systematic review. IEEE Transactions on Services Computing, v. 16, n. 2, p. 1505-1521, 2022. DOI: https://doi.org/10.1109/TSC.2022.3174475.

KHALIL, M. H.; SHETA, W. M.; ELMAGHRABY, A. S. Categorizing hardware failure in large scale cloud computing environment. In: 2016 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT), 2016, Limassol. Proceedings […]. Limassol: IEEE, 2016. DOI: https://doi.org/10.1109/ISSPIT.2016.7886058.

LI, Y.; JIANG, Z. M. J.; LI, H.; HASSAN, A. E.; HE, C.; HUANG, R.; ZENG, Z.; WANG, M.; CHEN, P. Predicting node failures in an ultra-large-scale cloud computing platform: an AIOps solution. ACM Transactions on Software Engineering Methodology, v. 29, n. 2, p. 1-24, 2020. DOI: https://doi.org/10.1145/3385187.

LIANG, C.; DENG, L.; ZHU, J.; CAO, Z.; LI, C. Disk failure prediction based on SW-disk feature engineering. In: 2022 IEEE INTERNATIONAL CONFERENCE ON BIG DATA SECURITY ON CLOUD (BIGDATASECURITY), IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE AND SMART COMPUTING, (HPSC) AND IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA AND SECURITY (IDS), 8., 2022, Jinan. Proceedings […]. Jinan: IEEE, 2022. DOI: https://doi.org/10.1109/BigDataSecurityHPSCIDS54978.2022.00027.

LIN, Q.; HSIEH, K.; DANG, Y.; ZHANG, H.; SUI, K.; XU, Y.; LOU, J.-G.; LI, C.; WU, Y.; YAO, R.; CHINTALAPATI, M.; ZHANG, D. Predicting node failure in cloud service systems. In: 2018 ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, 26., 2018, Lake Buena Vista. Proceedings […]. Lake Buena Vista: ACM, p. 480-490, 2018. DOI: https://doi.org/10.1145/3236024.3236060.

LIU, C.; DAI, L.; LAI, Y.; LAI, G.; MAO, W. Failure prediction of tasks in the cloud at an earlier stage: a solution based on domain information mining. Computing, v. 102, n. 2001-2003, 2020a. DOI: https://doi.org/10.1007/s00607-020-00800-1.

LIU, C.; HAN, J.; SHANG, Y.; LIU, C.; CHENG, B.; CHEN, J. Predicting of job failure in compute cloud based on online extreme learning machine: a comparative study. IEEE Access, v. 5, p. 9359-9368, 2017. DOI: https://doi.org/10.1109/ACCESS.2017.2706740.

LIU, D.; WANG, B.; LI, P.; STONES, R. J.; MARBACH, T. G.; WANG, G.; LIU, X.; LI, Z. Predicting hard drive failures for cloud storage systems. In: WEN, S.; ZOMAYA, A.; YANG, L. (eds.). Algorithms and Architectures for Parallel Processing. ICA3PP 2019. Lecture Notes in Computer Science (), v. 11944. Cham: Springer, 2020b. DOI: https://doi.org/10.1007/978-3-030-38991-8_25.

LIU, X.; HE, Y.; LIU, H.; ZHANG, J.; LIU, B.; PENG, X.; XU, J.; ZHANG, J.; ZHOU, A.; SUN, P.; ZHU, K.; NISHI, A.; ZHU, D.; ZHANG, K. Smart server crash prediction in cloud service data center. In: 2020 IEEE INTERSOCIETY CONFERENCE ON THERMAL AND THERMOMECHANICAL PHENOMENA IN ELECTRONIC SYSTEMS (ITherm), 19., 2020c, Orlando. Proceedings […]. Orlando: IEEE, 2020. DOI: https://doi.org/10.1109/ITherm45881.2020.9190321.

LUO, C.; ZHAO, P.; QIAO, B.; WU, Y.; ZHANG, H.; WU, W.; LU, W.; DANG, Y.; RAJMOHAN, S.; LIN, Q.; ZHANG, D. NTAM: Neighborhood-temporal attention model for disk failure prediction in cloud platforms. In: WEB CONFERENCE 2021 (WWW’ 21), 2021, Ljubljana. Proceedings […]. Ljubljana: ACM, p. 1181-1191, 2021. DOI: https://doi.org/10.1145/3442381.3449867.

MA, Y.; WU, S.; GONG, S.; XU, C. Artificial intelligence-based cloud data center fault detection method. In: 2020 IEEE JOINT INTERNATIONAL INFORMATION TECHNOLOGY AND ARTIFICIAL INTELLIGENCE CONFERENCE (ITAIC), 9., 2020, Chongqing. Proceedings […]. Chongqing: IEEE, 2020. DOI: https://doi.org/10.1109/ITAIC49862.2020.9338789.

MAJID, H.; ANUAR, S. A systematic literature review of failure prediction in production environment using machine learning technique. International Journal of Innovative Computing, v. 12, n. 1, p. 81-90, 2022. DOI: https://doi.org/10.11113/ijic.v12n1.348.

MARIANI, L.; MONNI, C.; PEZZÉ, M.; RIGANELLI, O.; XIN, R. Localizing faults in cloud systems. In: 2018 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE TESTING, VERIFICATION AND VALIDATION (ICST), 11., 2018, [S.l.]. Proceedings […]. [S.l.]: IEEE, 2018. DOI: https://doi.org/10.1109/ICST.2018.00034.

MELL, P.; GRANCE, T. The NIST definition of cloud computing. Recommendations of the National Institute of Standards and Technology. Gaithersburg: NIST, 2011. Disponível em: https://dl.acm.org/doi/book/10.5555/2206223. Acesso em: 15 dez. 2023.

MOHAMMED, B.; AWAN, I.; UGAIL, H.; YOUNAS, M. Failure prediction using machine learning in a virtualised HPC system and application. Cluster Computing, v. 22, p. 471-485, 2019. DOI: https://doi.org/10.1007/s10586-019-02917-1.

MOHAMMED, R.; RAWASHDEH, J.; ABDULLAH, M. Machine learning with oversampling and undersampling techniques: overview study and experimental results. In: 2020 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION SYSTEMS (ICICS), 11., 2020, Irbid. Proceedings […]. Irbid: IEEE, 2020. DOI: https://doi.org/10.1109/ICICS49469.2020.239556.

MONNI, C.; PEZZÈ, M.; PRISCO, G. An RBM anomaly detector for the cloud. In: 2019 IEEE Conference on Software Testing, Validation and Verification (ICST), 12.,2019, Xi’an. Proceedings […]. Xi’an: IEEE, 2019. DOI: https://doi.org/10.1109/ICST.2019.00024.

NAM, S.; HONG, J.; YOO, J.-H.; HONG, J. W.-K. Virtual machine failure prediction using log analysis. In: 2021 ASIA-PACIFIC NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM (APNOMS), 22., 2021, Tainan. Proceedings […]. Tainan: IEEE, 2021. DOI: https://doi.org/10.23919/APNOMS52696.2021.9562588.

NOTARO, P.; CARDOSO, J.; GERNDT, M. A survey of AIOps methods for failure management. ACM Transactions on Intelligent Systems and Technology, v. 12, n. 6, p. 1-45, 2021. DOI: https://doi.org/10.1145/3483424.

PADMAKUMARI, P.; UMAMAKESWARI, A. Task failure prediction using combine bagging ensemble (CBE) classification in cloud workflow. Wireless Personal Communications, v. 107, p. 23-40, 2019. DOI: https://doi.org/10.1007/s11277-019-06238-9.

PRATHIBHA, S. Investigating the performance of machine learning algorithms for improving fault tolerance for large scale workflow applications in cloud computing. In: 2019 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND KNOWLEDGE ECONOMY (ICCIKE), 2019, Dubai. Proceedings […]. Dubai: IEEE, 2019. DOI: https://doi.org/10.1109/ICCIKE47802.2019.9004379.

RAWAT, A.; BHADORIA, R. S. Accuracy estimation for fault classification in virtual machine using deep learning. In: 2021 INTERNATIONAL CONFERENCE ON SECURE CYBER COMPUTING AND COMMUNICATIONS (ICSCCC), 2., 2021, Jalandhar. Proceedings […]. Jalandhar: IEEE, 2021. DOI: https://doi.org/10.1109/ICSCCC51823.2021.9478157.

RAWAT, A.; SUSHIL, R.; AGARWAL, A.; SIKANDER, A. A new approach for VM failure prediction using stochastic model in cloud. IETE Journal of Research, v. 67, n. 2, p. 165-172, 2021. DOI: https://doi.org/10.1080/03772063.2018.1537814.

ROUMANI, Y.; NWANKPA, J. K. An empirical study on predicting cloud incidents. International Journal of Information Management, v. 47, p. 131-139, 2019. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.01.014.

SANTOS, V. A.; MANACERO, A.; LOBATO, R. S.; SPOLON, R.; CAVENAGHI, M. A. A systematic review of fault tolerance solutions for communication errors in open source cloud computing. In: 2020 IBERIAN CONFERENCE ON INFORMATION SYSTEMS AND TECHNOLOGIES (CISTI), 15., 2020, Seville. Proceedings […]. Seville: IEEE, 2020. DOI: https://doi.org/10.23919/CISTI49556.2020.9140933.

SAXENA, D.; SINGH, A. K. OFP-TM: an online VM failure prediction and tolerance model towards high availability of cloud computing environments. The Journal of Supercomputing, v. 78, p. 8003-8024, 2022a. DOI: https://doi.org/10.1007/s11227-021-04235-z.

SAXENA, D.; SINGH, A. K. VM failure prediction based intelligent resource management model for cloud environments. In: 2022 INTERNATIONAL CONFERENCE ON POWER, CONTROL AND COMPUTING TECHNOLOGIES (ICPC2T), 2., 2022, Raipur. Proceedings […]. Raipur: IEEE, 2022b. DOI: https://doi.org/10.1109/ICPC2T53885.2022.9777020.

SHAO, Y.; ZHANG, Y. A failure prediction method for spacecraft loads based on time series model. In: 2018 INTERNATIONAL CONFERENCE ON RELIABILITY, MAINTAINABILITY, AND SAFETY (ICRMS), 12., 2018, Shanghai. Proceedings […]. Shanghai: IEEE, 2018. DOI: https://doi.org/10.1109/ICRMS.2018.00059.

SHETTY, J.; SAJJAN, R.; G., S. Task resource usage analysis and failure prediction in cloud. In: 2019 INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE ENGINEERING (Confluence), 9., 2019, Noida. Proceedings […]. Noida: IEEE, 2019. DOI: https://doi.org/10.1109/CONFLUENCE.2019.8776612.

SINGH, D.; SINGH, B. Investigating the impact of data normalization on classification performance. Applied Soft Computing, v. 97, Part B, 105524, 2020. DOI: https://doi.org/10.1016/j.asoc.2019.105524.

SU, C.-J.; TSAI, L.-C.; HUANG, S.-F.; LI, Y. Deep learning-based real-time failure detection of storage devices. In: AHRAM, T. (eds.). Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, v. 965. Cham: Springer, 2020. DOI: https://doi.org/10.1007/978-3-030-20454-9_16.

TEHRANI, A. F.; SAFI-ESFAHANI, F. A threshold sensitive failure prediction method using support vector machine. Multiagent and Grid Systems, v. 13, n. 2, p. 97-111, 2017. DOI: https://dx.doi.org/10.3233/MGS-170263.

VU, D. D.; VU, X. T.; KIM, Y. Deep learning-based fault prediction in cloud system. In: 2021 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC), 2021, Jeju Island. Proceedings […]. Jeju Island: IEEE, 2021. DOI: https://doi.org/10.1109/ICTC52510.2021.9620802.

WILKES, J. Yet more Google compute cluster trace data. 2020. Google research blog. Disponível em: https://blog.research.google/2020/04/yet-more-google-compute-cluster-trace.html. Acesso em: 15 dez. 2023.

YANG, H.; KIM, Y. Design and implementation of machine learning-based fault prediction system in cloud infrastructure. Electronics, v. 11, n. 22, 3765, 2022. DOI: https://doi.org/10.3390/electronics11223765.

ZHANG, P.; WANG, Y.; MA, X.; XU, Y.; YAO, B.; ZHENG, X.; JIANG, L. Predicting DRAM-caused node unavailability in hyper-scale clouds. In: 2022 ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN), 52., 2022, Baltimore. Proceedings […]. Baltimore: IEEE, 2022. DOI: https://doi.org/10.1109/DSN53405.2022.00037.


DOI: http://dx.doi.org/10.18265/2447-9187a2022id8011

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