AI-Augmented Predictive Analytics for Proactive Cloud Infrastructure Management
DOI:
https://doi.org/10.55662/JST.2024.5407Keywords:
AI-augmented analytics, predictive modeling, cloud infrastructure, anomaly detectionAbstract
Cloud computing environments involvement in advanced management strategies to ensure optimal performance, cost efficiency, and reliability. Predictive analytics based on AI- augmentation is emerged as a transformative approach to proactive cloud infrastructure management which uses machine learning models and deep learning techniques to predict system failures, optimize resource allocation, and enhance security postures. The aim of this paper is to present a complete analysis of AI-driven predictive models, highlighting anomaly detection, fault prediction, workload forecasting, and self-healing mechanisms.
Readership Data
Refreshing Cached Analytics Data
The cached analytics data has become stale and www.thesciencebrigade.com is making a fresh request to fetch the latest data from Google Analytics. This may take 20-30 seconds depending on the server response time from Google Analytics. Please do not close the browser during this time. We appreciate your patience.
Downloads
References
J. Zhaoxue, "A Survey On Log Research Of AIOps: Methods and Trends," Mobile Networks and Applications, vol. 26, no. 6, pp. 2353–2364, Dec. 2021. https://doi.org/10.1007/s11036-021-01832-3
P. Notaro, "A Survey of AIOps Methods for Failure Management," ACM Transactions on Intelligent Systems and Technology, vol. 12, no. 6, pp. 1-45, Nov. 2021. https://doi.org/10.1145/3483424
H. Wang, "AIOPS Prediction for Hard Drive Failures Based on Stacking Ensemble Model," in Proceedings of the 10th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2020, pp. 0417-0423. https://doi.org/10.1109/CCWC47524.2020.9031232
J. Li, "HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOps," arXiv preprint arXiv:2211.06890, Nov. 2022.
W. Yang, "A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes," arXiv preprint arXiv:2209.14211, Sep. 2022.
M. Haenlein and A. Kaplan, "A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence," California Management Review, vol. 61, no. 4, pp. 5-14, July. 2019. https://doi.org/10.1177/0008125619864925
S. Sharma et al., "Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies," in CoNEXT '20: Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies, Nov. 2020, pp. 1-12.
J. Jagannath, K. Ramezanpour, and A. Jagannath, "Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning," in WiseML '22: Proceedings of the 2022 ACM Workshop on Wireless Security and Machine Learning, May 2022, pp. 25-30.
D. B. Rawat et al., Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation. Beverly, MA: Scrivener Publishing LLC, 2021.
W. Hummer and V. Muthusamy, "A Programming Model for Reusable, Platform-Independent, and Composable AI Workflows," presented at the IBM Programming Languages Day, Dec. 2018.
W. Hummer et al., "ModelOps: Cloud-based Lifecycle Management for Reliable and Trusted AI," in Proceedings of the IEEE International Conference on Cloud Engineering (IC2E), 2019, pp. 113-120. https://doi.org/10.1109/IC2E.2019.00025
Citation Metrics
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
License Terms
Ownership and Licensing:
Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
How to Cite
