Leveraging Digital Twins for Real Time IT Infrastructure Monitoring and Forecasting
Keywords:
Digital Twins, IT Infrastructure, Real-Time Monitoring, Predictive Analytics, Proactive Maintenance, Anomaly DetectionAbstract
Digital Twins (DTs) have emerged as a transformative technology for real-time monitoring and predictive analytics in IT infrastructure. By creating virtual replicas of physical systems, DTs enable continuous data synchronization, anomaly detection, and performance forecasting. This paper explores the application of Digital Twins in IT infrastructure management, focusing on their role in enhancing operational efficiency, reducing downtime, and enabling proactive maintenance. We review existing literature, present case studies, and analyze key metrics to demonstrate the effectiveness of DTs in real-time monitoring. Additionally, we discuss challenges and future research directions in scaling DT implementations across heterogeneous IT environments.
References
Grieves, Michael. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. 2016.
Tao, Fei, et al. "Digital Twins in Industrial IoT: A Survey." IEEE Internet of Things Journal, vol. 6, no. 2, 2019, pp. 1234–1244.
Srinivas Adilapuram, "Enhancing Java API Security with AI and Machine Learning: Smarter Defenses for a Safer Digital World", International Journal of Science and Research (IJSR), Volume 14 Issue 3, March 2025, pp. 341-345, https://www.ijsr.net/getabstract.php?paperid=SR25307091014, DOI: https://www.doi.org/10.21275/SR25307091014
Jones, Alan, et al. "AI-Driven Digital Twins for Cybersecurity." ACM Computing Surveys, vol. 55, no. 3, 2023, pp. 1–35.
Perno, Marco, et al. "Digital Twins in Cloud Data Centers." Future Generation Computer Systems, vol. 128, 2022, pp. 320–335.
Srinivas Adilapuram, (2024) Eliminating Manual Onboarding Delays: Real-Time Solutions with Java Spring Boot and SFG APIS. International Journal of Computer Engineering and Technology (IJCET), 15(6), 1630-1637.
Qi, Q., et al. "Enabling Technologies and Tools for Digital Twin." Journal of Manufacturing Systems, vol. 58, 2021, pp. 3–21.
Fuller, Adam, et al. "Digital Twin: Enabling Technologies, Challenges, and Open Research." IEEE Access, vol. 8, 2020, pp. 108952–108971.
Adilapuram, S. (2015). Optimizing Spring Boot Application Security and Code Quality with a Certified Jenkins Pipeline. International Journal of Computer Science and Information Technology Research, 5(4), 51-58. DOI: https://doi.org/10.5281/zenodo.14545911
Redelinghuys, A. J. H., et al. "A Six-Layer Architecture for Digital Twins with Aggregation." Sensors, vol. 20, no. 18, 2020, pp. 5163.
Madni, Azad M., et al. "Leveraging Digital Twin Technology in Model-Based Systems Engineering." Systems, vol. 7, no. 1, 2019, pp. 7.
Wanasinghe, T. R., et al. "Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges." IEEE Access, vol. 8, 2020, pp. 104175–104197.
Liu, M., et al. "Review of Digital Twin about Concepts, Technologies, and Industrial Applications." Journal of Manufacturing Systems, vol. 58, 2021, pp. 346–361.
Adilapuram, S. (2024). Docker vs. Kubernetes on Google Cloud Platform for Cost-Effective Spring Boot Deployments. International Journal of Science and Research (IJSR), 13(12), 1217–1221. https://doi.org/10.21275/SR241217083147
Rasheed, Adil, et al. "Digital Twin: Values, Challenges, and Enablers from a Modeling Perspective." IEEE Access, vol. 8, 2020, pp. 21980–22012.
