Role of AI and ML in Enhancing Self-Healing Capabilities, Including Predictive Analysis and Automated Recovery
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P106Keywords:
Self-healing systems, Artificial Intelligence, Machine Learning, Predictive Analytics, Automated Recovery, Fault DetectionAbstract
AI, machine learning (ML), and artificial intelligence are changing a lot of fields. One of the most promising places to use them is in self-healing systems. Self-healing capabilities: These are systems that can find problems, predict when they might happen, and fix themselves without any help from a person. This paper talks about how AI and ML can be used to make self-healing systems better by using predictive analysis, anomaly detection, and automated recovery. We talk about AI techniques like neural networks, decision trees, and reinforcement learning that are used to model and predict system failures. When using historical and real-time data to learn, ML algorithms can be used to predict a problem and start fixing it before it happens. The article also compares the current methods and suggests a structure that allows predictive analytics and recovery algorithms to work together in real time. To show how well and safely AI-driven self-healing computer systems work, we look at different real-world uses and case studies in areas like self-driving cars, cloud computing, and industrial automation. Some of the problems that come up are model accuracy, data quality, and moral issues. The proposed architecture and different ML models' performance are shown in tables and figures. The methodology relies on flowcharts and mathematical models. The paper concludes with prospective opportunities and the potential for the widespread application of AI in the development of self-healing ecosystems
References
[1] Salehie, M., & Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM transactions on autonomous and adaptive systems (TAAS), 4(2), 1-42.
[2] Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41-50.
[3] Psaier, H., & Dustdar, S. (2011). A survey on self-healing systems: approaches and systems. Computing, 91(1), 43-73.
[4] Çınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine Learning in Predictive Maintenance for Sustainable Smart Manufacturing in Industry 4.0. Sustainability, 12(19), 8211.
[5] Zhu, T., Ran, Y., Zhou, X., & Wen, Y. (2019). A survey of predictive maintenance: Systems, purposes and approaches. arXiv preprint arXiv:1912.07383.
[6] Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction (Vol. 1, No. 1, pp. 9-11). Cambridge: MIT press.
[7] Ratasich, D., Khalid, F., Geissler, F., Grosu, R., Shafique, M., Bartocci, E. (2018). A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems. arXiv. https://arxiv.org/abs/1810.06870.
[8] Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE transactions on intelligent transportation systems, 23(6), 4909-4926.
[9] Schneider, C., Barker, A., & Dobson, S. (2015). A survey of self‐healing systems frameworks. Software: Practice and Experience, 45(10), 1375-1398.
[10] Huo, Z., Mukherjee, M., Shu, L., Chen, Y., & Zhou, Z. (2016, September). Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants. In 2016 International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 1080-1085). IEEE.
[11] Carvalho, T. P., Soares, F. A., Vita, R., Francisco, R. D. P., Basto, J. P., & Alcalá, S. G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137, 106024.
[12] Paolanti, M., Romeo, L., Felicetti, A., Mancini, A., Frontoni, E., & Loncarski, J. (2018, July). Machine Learning Approach for Predictive Maintenance in Industry 4.0. In 2018, the 14th IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications (MESA) (pp. 1-6). IEEE.
[13] Ma, T., Ali, S., & Yue, T. (2021). Testing self-healing cyber-physical systems under uncertainty with reinforcement learning: an empirical study. Empirical Software Engineering, 26(3), 52.
[14] Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016, June). Data cleaning: Overview and emerging challenges. In Proceedings of the 2016 International Conference on Management of Data (pp. 2201-2206).
[15] De Menezes, F. S., Liska, G. R., Cirillo, M. A., & Vivanco, M. J. (2017). Data classification with binary response through the Boosting algorithm and logistic regression. Expert Systems with Applications, 69, 62-73.
[16] Wang, Y., Pham, D. T., & Ji, C. (2015). Self-healing composites: A review. Cogent Engineering, 2(1), 1075686.
[17] Knottnerus, J. A., van Weel, C., & Muris, J. W. (2002). Evaluation of diagnostic procedures. Bmj, 324(7335), 477-480.
[18] Ghosh, D., Sharman, R., Rao, H. R., & Upadhyaya, S. (2007). Self-healing systems—survey and synthesis. Decision support systems, 42(4), 2164-2185.
[19] Vasilescu, I., Kotay, K., Rus, D., Dunbabin, M., & Corke, P. (2005, November). Data collection, storage, and retrieval with an underwater sensor network. In Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems (pp. 154-165).
[20] Gupta, S., Al-Obaidi, S., & Ferrara, L. (2021). Meta-analysis and machine learning models to optimize the efficiency of the self-healing capacity of cementitious material. Materials, 14(16), 4437.
[21] Pappula, K. K., & Anasuri, S. (2020). A Domain-Specific Language for Automating Feature-Based Part Creation in Parametric CAD. International Journal of Emerging Research in Engineering and Technology, 1(3), 35-44. https://doi.org/10.63282/3050-922X.IJERET-V1I3P105
[22] Rahul, N. (2020). Vehicle and Property Loss Assessment with AI: Automating Damage Estimations in Claims. International Journal of Emerging Research in Engineering and Technology, 1(4), 38-46. https://doi.org/10.63282/3050-922X.IJERET-V1I4P105
[23] Enjam, G. R., & Chandragowda, S. C. (2020). Role-Based Access and Encryption in Multi-Tenant Insurance Architectures. International Journal of Emerging Trends in Computer Science and Information Technology, 1(4), 58-66. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I4P107
[24] Pappula, K. K., & Anasuri, S. (2021). API Composition at Scale: GraphQL Federation vs. REST Aggregation. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 54-64. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P107
[25] Pedda Muntala, P. S. R. (2021). Integrating AI with Oracle Fusion ERP for Autonomous Financial Close. International Journal of AI, BigData, Computational and Management Studies, 2(2), 76-86. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I2P109
[26] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[27] Enjam, G. R., Chandragowda, S. C., & Tekale, K. M. (2021). Loss Ratio Optimization using Data-Driven Portfolio Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 54-62. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P107
