Self-Healing Angular Architecture: AI-Driven Autonomous Error Recovery and System Resilience
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I3P122Keywords:
Angular, Self-Healing Systems, AI-driven Architecture, Error Recovery, Resilience Engineering, Frontend Intelligence, Autonomous SystemsAbstract
The trend to more sophisticated modern Angular applications due to the fast-paced user interfaces, integration of distributed micro services, and the need to handle real-time data processing, has been one of the main contributors to the increased threats of runtime failures, inconsistencies in states, and poor user experience. Conventional error-handling systems, which depend on the exception handling of static exceptional cases and human-led debugging efforts are being observed to no longer be adequate to provide reliable system execution in large and mission-critical systems. The situation has been experienced to make it apparent that there is an urgent need to have smart and autonomous systems that will have the capability to identify, analyze, and fix errors in real-time without human interference. The paper introduces a new Angular architecture that runs on AI with the purpose of self-healing of frontend applications, that is, allowing autonomous error detection, root cause, and recovery. Its architecture combines anomaly detection models using machine learning with an event-based monitoring system to monitor the behavior of the application continuously, formulate deviations of the expected patterns, and initiate adaptive remedial measures. The system can also be improved by providing a feedback-centered learning loop that allows the system to improve the accuracy of detection and efficiency of recovery through time. The major contributions of the work are: (1) an error detection engine working in real-time and capable of detecting both known and unknown failure modes based on AI techniques; (2) an automated remediation system that implements intelligent remediation measures (component initialization, state rollback, and API retry mechanisms); (3) a resilient-oriented architecture framework that enhances system availability and resilience in dynamically and unpredictably changing environments Through experimental testing it becomes clear that the suggested solution is much faster in error solving measures, system time is also minimized and general system resilience of the application is increased in comparison to more conventional frontend architecture. The findings demonstrate that integrating self-healing -ability into Angular apps may achieve a complete rethinking of frontend engineering by offering adaptive, fault-tolerant, intelligent user interface apps that are appropriate to next-generation enterprise apps.
References
[1] Chennareddy, R. K. (2021). Designing Data and Analytics Ecosystems for High Volume Transaction Processing Applications. International Journal of AI, BigData, Computational and Management Studies, 2(2), 95-106.
[2] Sethuraman, P., & Chennareddy, R. K. (2022). Machine Learning Assisted Design of Wireless Access Systems for Reliable and Low-Latency Financial and Smart Commerce Services. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 133-142.
[3] Sethuraman, P., & Chennareddy, R. K. (2022). Intelligent Vehicular Traffic Flow Prediction Using Learning-Based Spatio-Temporal Models for Data-Driven Wireless Transportation and Urban Analytics Systems. International Journal of Emerging Trends in Computer Science and Information Technology, 3(2), 111-121.
[4] Sethuraman, P. (2022). Latency-Aware Scheduling and Resource Control Algorithms for Emergency and Public Safety Wireless Networks. International Journal of Emerging Research in Engineering and Technology, 3(4), 133-140.
[5] Sethuraman, P., & Chennareddy, R. K. (2023). AI-Based Fraud Detection and Prevention at the Radio Access Network: Architectures and Mechanisms for Financial Wireless Service. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(4), 132-141.
[6] Chennareddy, R. K., & Sethuraman, P. (2023). Enterprise and RAN-Aware Data and Analytics Platforms for Mission-Critical and Low-Latency Digital Services. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 184-192.
[7] Sethuraman, P., & Chennareddy, R. K. (2023). System-Level Design and Orchestration of Large-Scale Cellular Access Networks for Regulatory-Compliant Financial Services. International Journal of Emerging Research in Engineering and Technology, 4(3), 140-150.
[8] Chennareddy, R. K. (2023). Enterprise-Scale AI and Analytics Strategy for End-to-End Business Transformation across Global Organizations. International Journal of AI, BigData, Computational and Management Studies, 4(3), 134-145.
[9] Ibitoye¹, J. S., & Ayobami, F. E. (2022). Self-healing networks using AI-driven root cause analysis for cyber recovery.
[10] Hadjisotiriou, C., Andrew, R., & Marshall, K. (2004). Debugging and Error Handling. In ASP. NET Web Development with Macromedia Dreamweaver MX 2004 (pp. 265-285). Berkeley, CA: Apress.
[11] Patel, J., & Shah, H. (2021). Software engineering revolutionized by machine learning-powered self-healing systems. Int. Res. J. Eng. Appl. Sci., 9(1), 43-49.
[12] Ghosh, D., Sharman, R., Rao, H. R., & Upadhyaya, S. (2007). Self-healing systems—survey and synthesis. Decision support systems, 42(4), 2164-2185.
[13] Jangam, S. K. (2022). Self-Healing Autonomous Software Code Development. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 42-52.
[14] Bhanage, D. A., Pawar, A. V., & Kotecha, K. (2021). 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, 9, 156392-156421.
[15] Tantalaki, N., Souravlas, S., & Roumeliotis, M. (2020). A review on big data real-time stream processing and its scheduling techniques. International Journal of Parallel, Emergent and Distributed Systems, 35(5), 571-601.
[16] Liu, X., & Buyya, R. (2020). Resource management and scheduling in distributed stream processing systems: a taxonomy, review, and future directions. ACM Computing Surveys (CSUR), 53(3), 1-41.
[17] Dashofy, E. M., Van der Hoek, A., & Taylor, R. N. (2002, November). Towards architecture-based self-healing systems. In Proceedings of the first workshop on Self-healing systems (pp. 21-26).
[18] Kawamura, R., & Tokizawa, I. (1995). Self-healing virtual path architecture in ATM networks. IEEE Communications Magazine, 33(9), 72-79.
[19] Rajput, P. K., & Sikka, G. (2021). Multi-agent architecture for fault recovery in self-healing systems. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2849-2866.
[20] Mohamudally, N., & Peermamode-Mohaboob, M. (2018). Building an anomaly detection engine (ADE) for IoT smart applications. Procedia computer science, 134, 10-17.
[21] Gulenko, A., Wallschläger, M., Schmidt, F., Kao, O., & Liu, F. (2016). A system architecture for real-time anomaly detection in large-scale nfv systems. Procedia Computer Science, 94, 491-496.
[22] Khriji, S., Benbelgacem, Y., Chéour, R., Houssaini, D. E., & Kanoun, O. (2022). Design and implementation of a cloud-based event-driven architecture for real-time data processing in wireless sensor networks. The Journal of Supercomputing, 78(3), 3374-3401.
[23] Seiger, R., Huber, S., & Schlegel, T. (2018). Toward an execution system for self-healing workflows in cyber-physical systems. Software & Systems Modeling, 17(2), 551-572.
[24] Satyanarayanan, A. (2022). Optimizing Data Quality in Real-Time: A Self-Healing Pipeline Approach. International Journal of AI, BigData, Computational and Management Studies, 3(2), 62-80.
[25] Liang, H., & Yin, X. (2023). Self-healing control: Review, framework, and prospect. IEEE Access, 11, 79495-79512.
