Development of ANFIS-Based Hard Drive Failure Prediction Model for Cloud Platforms Using Intelligent Techniques
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Page Numbers:
178-184
Digital Object Identifier:
10.58578/mjaei.v3i2.9185
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Authors:I. I. Ahmad1*, J. D. Jiya2, MA. Baba3
Copyright :Authors retain copyright and grant the journal right of first publication.
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Abstract
Hard drive failures remain a critical reliability concern in large-scale cloud data centres because they can lead to data loss, service downtime, and increased operational costs. Traditional threshold-based monitoring techniques often fail to capture nonlinear relationships among hard drive health indicators and may produce high false-positive rates. This study presents a conceptual framework for developing an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based hard drive failure prediction model using selected Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes. It further examines the potential impact of key SMART indicators on predictive performance. By integrating fuzzy logic reasoning with neural network learning, the proposed framework is designed to improve predictive accuracy while maintaining interpretability. The study concludes that an ANFIS-based prediction framework can support proactive maintenance strategies for cloud service providers by enabling earlier identification of potential hard drive failures. This framework contributes to the development of intelligent predictive maintenance systems in cloud computing environments and offers practical implications for improving system reliability, reducing downtime, and enhancing operational efficiency.
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References
Amekraz, S., & Hadi, Y. (2022). Hybrid neuro-fuzzy approach for workload prediction in cloud computing environments. Journal of Cloud Computing, 11(1), 45–60.
Chopra, V., Sharma, R., & Gupta, A. (2021). Adaptive neuro-fuzzy inference systems: A review of applications and advancements. Artificial Intelligence Review, 54(4), 3211–3245.
Ganesh, R., Kumar, S., & Babu, P. (2022). Handling imbalanced SMART datasets for hard disk failure prediction using ADASYN and feature extraction. IEEE Access, 10, 88921–88935.
Gheibi, A., Motahari, S., & Ahmadianfar, I. (2023). Neuro-fuzzy modelling for predictive maintenance in industrial systems. Engineering Applications of Artificial Intelligence, 120, Article 105865.
Hakim, M., Rahman, T., & Sada, A. (2022). ANFIS-based equipment failure prediction in data center infrastructure. Computers & Electrical Engineering, 99, Article 107745.
Islam, M. S., Hassan, M., & Raza, A. (2023). Reliability challenges in large-scale cloud storage infrastructures. Future Generation Computer Systems, 144, 54–66.
Li, J., Wang, X., & Pecht, M. (2014). Classification and regression trees for hard disk drive failure prediction. Reliability Engineering & System Safety, 130, 1–11.
Li, Y., & Huang, K. (2024). Failure characteristics analysis of large-scale hard disk drives in cloud data centers. IEEE Transactions on Cloud Computing, 12(1), 210–223.
Mazandarani, M., & Li, X. (2020). A comprehensive review of ANFIS architectures and learning strategies. Neurocomputing, 401, 196–213.
Mohapatra, P., Roy, S., & Sahoo, B. (2023). LSTM-based prediction of remaining useful life of hard disk drives using SMART data. IEEE Transactions on Reliability, 72(2), 480–492.
Ozah, F., Ahmed, M., & Bello, T. (2023). Comparative evaluation of regression models for hard drive failure forecasting using SMART attributes. Journal of Big Data, 10(1), Article 134.
Pecht, M., & Elburn, T. (2021). Temperature effects on hard disk drive reliability in large-scale storage systems. Microelectronics Reliability, 117, Article 113673.
Tomer, S., Singh, R., & Sharma, P. (2021). Hard disk failure analysis using SMART data in data centers. IEEE Access, 9, 134578–134589.