Predictive Intelligence for Customer Operations and Data Center Delivery Excellence
Keywords:
Autonomous Service Intelligence Fabric, Operations Research, Predictive Analytics, Inference Engine, Autonomous Operations Research Engine, Service Intelligence, Enterprise IT Service Management, Data Center Optimization, Decision Intelligence, Process Automation, Customer Operations Management, Predictive Operations.Abstract
Operations Research (OR) and Aniline solution design and automation can gain new power through Inference Engines, Predictive Analytics, and the design and use of an Autonomous Service Intelligence Fabric (ASIF). An ASIF collects data and monitors processes to make predictions, reason about future possibilities, and propose corrective actions. It also acts as an Autonomous Operations Research Engine (AORE)—an intelligent assistant that leverages ancient Theory of Know-How formulation—to help enterprise IT management continuously refine and enhance Enterprise IT Services, Service Delivery, and Customer Operations Management Bus Processes Activities. Such technology sits alongside, supports, and enhances existing Operations Research Technology and Know-How. The initial focus on Data Centre Delivery Optimization supports rapid deployment and use. Data, information, and solutions flow from customer subject-domain experts, enterprise Data Centre Services Act Delivery Content-Solution Users and Suppliers Expertise and Aniline Solution-Decision centres, with support from Aniline Solution Design Automation experts and tools. Future research extends capabilities, moves to services for Predictive Customer Operations including Supply Chain, and leads to the creation of new textbooks on Predictive Analytic Applications using Know-How and Predictive Customer Operations Business Process Activities through Aniline Solution Training.
References
1. Al-Sayed, M. M. (2022). Workload time series cumulative prediction mechanism for cloud resources using neural machine translation technique. Journal of Grid Computing, 20, Article 16.
2. Becker, S., Schmidt, F., Gulenko, A., Acker, A., & Kao, O. (2020). Towards AIOps in edge computing environments. In 2020 IEEE International Conference on Big Data (Big Data). IEEE.
3. Chen, J., Du, T., & Xiao, G. (2021). A multi-objective optimization for resource allocation of emergent demands in cloud computing. Journal of Cloud Computing, 10, Article 20.
4. Anil Lokesh Gadi, “Connected Mobility Platforms and Their Impact on Urban Transport Planning and Service Design,” International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI: 10.17148/IJIREEICE.2019.71207
5. Chen, Z., Kang, Y., Li, L., Zhang, X., Zhang, H., Xu, H., Zhou, Y., Li, Y., Sun, J., Xu, Z., Dang, Y., Gao, F., Zhao, P., Qiao, B., Lin, Q., Zhang, D., & Lyu, M. R. (2020). Towards intelligent incident management: Why we need it and how we make it. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Association for Computing Machinery.
6. Daradkeh, T., & Agarwal, A. (2022). Cloud workload and data center analytical modeling and optimization using deep machine learning. Network, 2(4), 643–669.
7. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
8. Dong, W. (2022). AIOps architecture in data center site infrastructure monitoring. Computational Intelligence and Neuroscience, 2022, Article 1988990.
9. Fard, M. V., Sahafi, A., Rahmani, A. M., & Sheikholharam Mashhadi, P. (2020). Resource allocation mechanisms in cloud computing: A systematic literature review. IET Software, 14(6), 638–653.
10. Funika, W., Koperek, P., & Kitowski, J. (2022). Automated cloud resources provisioning with the use of the proximal policy optimization. The Journal of Supercomputing, 79, 6674–6704.
11. Sasi Kumar Kolla. (2021). Adaptive Graph-Based Framework for Secure and Efficient Distributed Data Processing. Journal of Computational Analysis and Applications (JoCAAA), 29(6), 1519–1523. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5860
12. Khan, T., Tian, W., & Buyya, R. (2022). Machine learning (ML)-centric resource management in cloud computing: A review and future directions. Journal of Network and Computer Applications, 204, Article 103405.
13. Manukonda, K. R. R. (2021). ENHANCING SOFTWARE QUALITY ASSURANCE THROUGH MACHINE LEARNING: TECHNIQUES, APPLICATIONS, AND CASE STUDIES. JOURNAL OF BASIC SCIENCE AND ENGINEERING, 18(1), 202-210.
14. Kohyarnejadfard, I., Aloise, D., Azhari, S. V., & Dagenais, M. R. (2022). Anomaly detection in microservice environments using distributed tracing data analysis and natural language processing. Journal of Cloud Computing, 11, Article 25.
15. Li, Y., Zhang, X., He, S., Chen, Z., Kang, Y., Liu, J., Li, L., Dang, Y., Gao, F., Xu, Z., Rajmohan, S., Lin, Q., Zhang, D., & Lyu, M. R. (2022). An intelligent framework for timely, accurate, and comprehensive cloud incident detection. ACM SIGOPS Operating Systems Review, 56(1), 1–7.
16. Manzoor, M. F., Abid, A., Farooq, M. S., Nawaz, N. A., & Farooq, U. (2020). Resource allocation techniques in cloud computing: A review and future directions. Elektronika Ir Elektrotechnika, 26(6), 40–51.
17. RAMI, R. M. K. (2020). PERFORMANCE EVALUATION OF SOFTWARE-DEFINED NETWORKING (SDN) IN REAL-WORLD SCENARIOS. INTERNATIONAL JOURNAL, 9(3), 1665-1671.
18. Nawrocki, P., & Osypanka, P. (2021). Cloud resource demand prediction using machine learning in the context of QoS parameters. Journal of Grid Computing, 19, Article 20.
19. Osypanka, P., & Nawrocki, P. (2022). Resource usage cost optimization in cloud computing using machine learning. IEEE Transactions on Cloud Computing, 10(3), 2079–2089.
20. Mashetty, S., & Mukesh, A. (2022). Application of Machine Learning in Loan Default Forecasting. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 123-132. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I4P114
21. Panwar, R., & Supriya, M. (2022). Dynamic resource provisioning for service-based cloud applications: A Bayesian learning approach. Journal of Parallel and Distributed Computing, 166, 1–15.
22. Rossi, A., Visentin, A., Prestwich, S. D., & Brown, K. N. (2022). Bayesian uncertainty modelling for cloud workload prediction. In 2022 IEEE International Conference on Cloud Computing (CLOUD). IEEE.
23. Saravanan, G., & Yuvaraj, N. (2021). Cloud resource optimization based on Poisson linear deep gradient learning for mobile cloud computing. Journal of Intelligent & Fuzzy Systems, 40(1), 787–797.
24. Shahzad, F., Mannan, A., Javed, A. R., Almadhor, A. S., Baker, T., & Al-Jumeily, D. (2022). Cloud-based multiclass anomaly detection and categorization using ensemble learning. Journal of Cloud Computing, 11, Article 74.
25. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
26. Shi, R., & Jiang, C. (2022). Three-way ensemble prediction for workload in the data center. IEEE Access, 10, 1–12.
27. Swain, A. K., & Garza, V. R. (2022). Key factors in achieving service level agreements (SLA) for information technology (IT) incident resolution. Information Systems Frontiers.
28. Thota, R. C. (2022). AIOps in cloud computing: Automation performance monitoring with AI and New Relic. International Journal of Scientific Research and Management, 10(3), 809–817.
29. A multi-output prediction model for physical machine resource usage in cloud data centers. (2022). Future Generation Computer Systems, 130, 292–306.
30. A literature review and taxonomy on workload prediction in cloud data center. (2020). In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE.
31. Integrated deep learning method for workload and resource prediction in cloud systems. (2021). Neurocomputing, 424, 35–48.
32. Self-directed learning-based workload forecasting model for cloud resource management. (2021). Information Sciences, 543, 345–366.
