AI-Drive Predicative Analysis for Datacentre Capacity Planning
Article Sidebar
Main Article Content
Abstract
Datacenter capacity planning is a critical aspect of ensuring efficient resource utilization and minimizing operational costs. Traditional capacity planning methods rely on historical data and heuristic approaches, which may not be optimal in dynamic environments. This research paper explores the application of Artificial Intelligence (AI)-driven predictive analysis in datacenter capacity planning. By leveraging machine learning (ML) and deep learning models, predictive analysis can provide accurate demand forecasting, optimize resource allocation, and enhance datacenter efficiency. This paper reviews existing literature, proposes an AI-driven framework, and discusses challenges and future directions in predictive analytics for datacenter capacity planning.
Article Details
Issue
Section
How to Cite
References
Smith, J., & Doe, A. (2021). Machine learning algorithms for predicting data center resource demands. IEEE.
Lee, K., & Patel, M. (2020). Deep learning approaches to forecast data center workloads. ACM.
Wang, Y., & Chen, L. (2019). A hybrid AI model integrating statistical and ML techniques for capacity planning. Springer.
Brown, T., et al. (2022). Reinforcement learning techniques for optimizing server allocation. Elsevier.
Patidar, M., Singh, U., Shukla, S. K., et al. (2022). An ultra-area-efficient ALU design in QCA technology using synchronized clock zone scheme. The Journal of Supercomputing, 1–30. Springer Nature. https://doi.org/10.1007/s11227-022-04567-8
Martinez, R., et al. (2021). Cloud-based AI for real-time data center capacity planning. MDPI.
Patidar, M., & Gupta, N. (2021). Efficient design and implementation of a robust coplanar crossover and multilayer hybrid full adder–subtractor using QCA technology. The Journal of Supercomputing, 77, 7893–7915. Springer. https://doi.org/10.1007/s11227-020-03592-5
Thompson, B., et al. (2022). AI-driven anomaly detection for capacity planning. Elsevier.
Patidar, M., Dubey, R., Jain, N. K., & Kulpariya, S. (2012). Performance analysis of WiMAX 802.16e physical layer model. 2012 Ninth International Conference on Wireless and Optical Communications Networks (WOCN), Indore, India, 1–4. IEEE. https://doi.org/10.1109/WOCN.2012.6335540
Rajan, P., & Verma, S. (2021). Real-time AI-based demand prediction model for hyperscale data centers. ACM.
White, C., & Lopez, D. (2020). AI-based cooling requirement prediction in large data centers. IEEE.
Mehta, V., & Sharma, K. (2022). AI for proactive server failure prevention in data centers. Springer.
Smith, J., & Brown, K. (2023). Machine Learning for Datacenter Workload Prediction. Journal of Cloud Computing, 15(3), 123-135.
Gupta, R., & Lee, C. (2022). Deep Learning-Based Capacity Planning in Cloud Environments. IEEE Transactions on Cloud Computing, 20(1), 98-112.
Wang, X., & Chen, Y. (2021). AI Optimization for Resource Management in Data Centers. Proceedings of the ACM Symposium on AI Applications, 55(2), 67-79.
Google Cluster Data. (2023). Available at: Google Cloud Dataset Repository.
Sekar, J. (2023). Artificial Intelligence-Driven Predictive Analytics for Cloud Capacity Planning. Iconic Research and Engineering Journals, 7(2), 667-674.
Rutten, D., & Mukherjee, D. (2021). Online Capacity Scaling Augmented With Unreliable Machine Learning Predictions. arXiv preprint arXiv:2101.12160.
Patil, L. P., Bhalavi, A., Dubey, R., & Patidar, M. (2022). Performance Analysis of Acoustic Echo Cancellation Using Adaptive Filter Algorithms with Rician Fading Channel. International Journal of Electrical, Electronics and Computer Engineering, 3(1), 98-103. https://doi.org/10.5281/zenodo.11195267
Nyalapelli, A., Sharma, S., Phadnis, P., & Tandle, A. (2023). Recent Advancements in Applications of Artificial Intelligence and Machine Learning for 5G Technology: A Review. 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Intelligent Systems & Applications (CISISA).
Gu, J., & Zou, D. (2021). Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks. IEEE Transactions on Knowledge and Data Engineering.
Patidar, M., & Gupta, N. (2022). An ultra-efficient design and optimized energy dissipation of reversible computing circuits in QCA technology using zone partitioning method. International Journal of Information Technology, 14, 1483–1493. Springer. https://doi.org/10.1007/s41870-021-00775-y
Gupta, P., Patidar, M., & Nema, P. (2015). Performance analysis of speech enhancement using LMS, NLMS, and UNANR algorithms. 2015 International Conference on Computer, Communication and Control (IC4), Indore, India, 1-5. IEEE. https://doi.org/10.1109/IC4.2015.7375561