Real-Time Performance Monitoring with AI
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P111Keywords:
Real-time performance monitoring, Artificial Intelligence, Machine Learning, Deep Learning, Predictive Analytics, Anomaly Detection, System OptimizationAbstract
Real-time performance monitoring (RTPM) has become an important element in improving the efficiency of working of modern computing systems and industrial processes. RTPM has undergone dramatic improvements with the introduction of artificial intelligence (AI), since it is now able to do predictive analytics, anomaly detection, and adaptive performance tuning. This essay discusses the concept of AI-based RTPM systems that describe their designs, procedures, and utilization in various fields of application. Our research is on machine learning and deep learning mechanisms to keep track of system performance indicators, such as CPU usage, network delays, memory usage, and process industry variables. AI integration can perform monitoring of the performances which were done manually on the basis of the manual analysis before, so, the response time becomes shorter and the accuracy increases. We also talk about the difficulties with the implementation of AI-based RTPM, including non-homogeneity of the data, related real-time processing limitations, and interpretability of the AI models. In addition, the paper provides a comparative discussion between the standard monitoring systems and AI-based methods and shows that the latter performs significantly better in terms of prediction quality and their adaptability. The use of AI in RTPM systems has been proven to improve operational productivity and minimize downtime by large-scale simulations and case studies. Lastly, future research and development suggestions in AI-enabled performance monitoring are given that require robust, scalable and explainable AI models to support increased needs of dynamic operational environments
Downloads
References
[1] Zhang, Y., Zheng, Z., & Lyu, M. R. (2012, April). Real-time performance prediction for cloud components. In 2012 IEEE 15th International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing Workshops (pp. 106-111). IEEE.
[2] Tekale, K. M., & Rahul, N. (2022). AI and Predictive Analytics in Underwriting, 2022 Advancements in Machine Learning for Loss Prediction and Customer Segmentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 95-113. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I1P111
[3] Saxena, D., Kumar, J., Singh, A. K., & Schmid, S. (2023). Performance analysis of machine learning centered workload prediction models for cloud. IEEE Transactions on Parallel and Distributed Systems, 34(4), 1313-1330.
[4] Bian, J., Al Arafat, A., Xiong, H., Li, J., Li, L., Chen, H., ... & Guo, Z. (2022). Machine learning in real-time Internet of Things (IoT) systems: A survey. IEEE Internet of Things Journal, 9(11), 8364-8386.
[5] Thallam, N. S. T. (2020). Comparative Analysis of Data Warehousing Solutions: AWS Redshift vs. Snowflake vs. Google BigQuery. European Journal of Advances in Engineering and Technology, 7(12), 133-141.
[6] Tekale, K. M. (2022). Claims Optimization in a High-Inflation Environment Provide Frameworks for Leveraging Automation and Predictive Analytics to Reduce Claims Leakage and Accelerate Settlements. International Journal of Emerging Research in Engineering and Technology, 3(2), 110-122. https://doi.org/10.63282/3050-922X.IJERET-V3I2P112
[7] Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., ... & Amodei, D. (2018). The malicious use of artificial intelligence: Forecasting, prevention, and mitigation. arXiv preprint arXiv:1802.07228.
[8] Sehrawat, S. K. (2023). The role of artificial intelligence in ERP automation: state-of-the-art and future directions. Trans Latest Trends Artif Intell, 4(4).
[9] Tekale, K. M., & Rahul, N. (2023). Blockchain and Smart Contracts in Claims Settlement. International Journal of Emerging Trends in Computer Science and Information Technology, 4(2), 121-130. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I2P112
[10] Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and prediction: The disruptive economics of artificial intelligence. Harvard Business Press.
[11] Sousa Tomé, E., Ribeiro, R. P., Dutra, I., & Rodrigues, A. (2023). An online anomaly detection approach for fault detection on fire alarm systems. Sensors, 23(10), 4902.
[12] Zhang, B., Sconyers, C., Byington, C., Patrick, R., Orchard, M., & Vachtsevanos, G. (2008, October). Anomaly detection: A robust approach to detection of unanticipated faults. In 2008 International Conference on Prognostics and Health Management (pp. 1-8). IEEE.
[13] Tekale, K. M. (2023). Cyber Insurance Evolution: Addressing Ransomware and Supply Chain Risks. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 124-133. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P113
[14] Iwanicki, K. (2018, July). A distributed systems perspective on industrial IoT. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (pp. 1164-1170). IEEE.
[15] Balis, B., Brzoza-Woch, R., Bubak, M., Kasztelnik, M., Kwolek, B., Nawrocki, P., ... & Zielinski, K. (2018). Holistic approach to management of IT infrastructure for environmental monitoring and decision support systems with urgent computing capabilities. Future Generation Computer Systems, 79, 128-143.
[16] Venkata SK Settibathini. Data Privacy Compliance in SAP Finance: A GDPR (General Data Protection Regulation) Perspective. International Journal of Interdisciplinary Finance Insights, 2023/6, 2(2), https://injmr.com/index.php/ijifi/article/view/45/13
[17] Tekale, K. M., Enjam, G. R., & Rahul, N. (2023). AI Risk Coverage: Designing New Products to Cover Liability from AI Model Failures or Biased Algorithmic Decisions. International Journal of AI, BigData, Computational and Management Studies, 4(1), 137-146. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I1P114
[18] Ghosn, M., Dueñas-Osorio, L., Frangopol, D. M., McAllister, T. P., Bocchini, P., Manuel, L., ... & Tsiatas, G. (2016). Performance indicators for structural systems and infrastructure networks. Journal of Structural Engineering, 142(9), F4016003.
[19] Kulasekhara Reddy Kotte. 2022. ACCOUNTS PAYABLE AND SUPPLIER RELATIONSHIPS: OPTIMIZING PAYMENT CYCLES TO ENHANCE VENDOR PARTNERSHIPS. International Journal of Advances in Engineering Research , 24(6), PP – 14-24, https://www.ijaer.com/admin/upload/02%20Kulasekhara%20Reddy%20Kotte%2001468.pdf
[20] Villegas-Ch, W., García-Ortiz, J., & Sánchez-Viteri, S. (2024). Toward intelligent monitoring in IoT: AI applications for real-time analysis and prediction. IEEE Access, 12, 40368-40386.
[21] Sternin, E. (1985). Data acquisition and processing: a systems approach. Review of scientific instruments, 56(11), 2043-2049.
[22] Emilio, M. D. P. (2013). Data acquisition systems. Fundamentals to Applied Design; Springer: New York, NY, USA.
[23] Muhammad, I. (2015). Supervised machine learning approaches: A survey. ICTACT Journal on Soft Computing.
[24] Gopi Chand Vegineni. 2022. Intelligent UI Designs for State Government Applications: Fostering Inclusion without AI and ML, Journal of Advances in Developmental Research, 13(1), PP – 1-13, https://www.ijaidr.com/research-paper.php?id=1454
[25] Tekale, K. M., & Enjam, G. reddy. (2023). Advanced Telematics & Connected-Car Data. International Journal of Emerging Trends in Computer Science and Information Technology, 4(1), 124-132. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I1P114
[26] Von Rueden, L., Mayer, S., Beckh, K., Georgiev, B., Giesselbach, S., Heese, R., ... & Schuecker, J. (2021). Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems. IEEE Transactions on Knowledge and Data Engineering, 35(1), 614-633.
[27] Wang, F., & Liu, J. (2010). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials, 13(4), 673-687.
[28] Vermesan, O., Coppola, M., Bahr, R., Bellmann, R. O., Martinsen, J. E., Kristoffersen, A., ... & Lindberg, D. (2022). An intelligent real-time edge processing maintenance system for industrial manufacturing, control, and diagnostic. Frontiers in Chemical Engineering, 4, 900096.
[29] Chakroun, I., Melab, N., Mezmaz, M., & Tuyttens, D. (2013). Combining multi-core and GPU computing for solving combinatorial optimization problems. Journal of Parallel and Distributed Computing, 73(12), 1563-1577.
[30] Srinath, K. R. (2017). Python–the fastest growing programming language. International Research Journal of Engineering and Technology, 4(12), 354-357.
[31] Qin, S. J. (1998). Control performance monitoring—a review and assessment. Computers & Chemical Engineering, 23(2), 173-186.
[32] Kulasekhara Reddy Kotte. 2023. Integrating Cybersecurity and Real-Time Analytics in Treasury Management: Enhancing Liquidity, Optimizing Working Capital, and Mitigating Financial Risks. International Journal of Professional Studies, 16(1), PP – 61 - 69, https://www.ijps.in/paper.php?id=193
[33] Thallam, N. S. T. (2022). Sustainable Cloud Computing: Reducing Carbon Footprint in Large-Scale Cloud Infrastructures. Journal of Scientific and Engineering Research, 9(1), 217-224.
