Intelligent Cloud Operations: Enhancing Infrastructure Management through Multi-Modal Learning and Predictive Analytics
Abstract
Managing cloud infrastructure effectively requires a blend of predictive insights and real-time operational awareness. This paper introduces an Intelligent Cloud Operations (ICO) framework that leverages multi-modal learning and predictive analytics to enhance infrastructure management. The proposed system integrates natural language understanding (NLU) with real-time resource monitoring and historical data analysis to improve decision-making and operational efficiency. By combining structured data from cloud metrics with unstructured user inputs, the framework enables dynamic resource allocation, anomaly detection, and fault resolution. The ICO architecture employs deep learning models for NLU and predictive analytics to anticipate infrastructure failures and optimize performance proactively. Experimental results demonstrate improved fault detection rates, faster response times, and enhanced resource utilization. The proposed framework advances cloud operations by merging cognitive understanding with predictive insights, enabling intelligent and adaptive infrastructure management.
References
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., ... & Riedel, S. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474.
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., ... & Yih, W.-T. (2020). Dense passage retrieval for open-domain question answering. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 6769–6781.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., ... & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
Gao, L., & Callan, J. (2021). Is your language model connected to the world? Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 4730–4745.
Xie, Q., Dai, Z., Hovy, E., & Sun, Z. (2021). Analyzing and improving the performance of retrieval-augmented generation models. Proceedings of the 2021 Conference on Neural Information Processing Systems, 6451–6463.
Zhang, Y., Balog, K., & Lin, J. (2021). Conversations with documents: An exploration of text-based knowledge retrieval models. Information Retrieval Journal, 24(2), 137–158.
Cheng, G., Zhang, Y., & Qu, Y. (2020). Knowledge graph-enhanced open-domain question answering. Proceedings of the 29th ACM International Conference on Information and Knowledge Management, 2521–2524.
Glaeser, L., & König, A. (2021). Cloud infrastructure management using deep learning models. Journal of Cloud Computing: Advances, Systems and Applications, 10(1), 1–15.
