From Visibility to Intelligence: AI for Cloud Infrastructure Observability Transformation and Enhancement
Keywords:
Artificial Intelligence (AI), cloud Infrastructure Visibility, Real-Time Monitoring, Predictive Analytics, Resource OptimizationAbstract
Network devices, hardware like CPU's, GPU's, Storage, Memory, application servers, database hardware are some of cloud infrastructure that play an important role in the modern enterprise. While organizations extend their operations worldwide, the management of issues related to such systems becomes challenging. Conventional monitoring techniques where visibility alone defines the state of the equipment are hardly effective for managing these intricate systems. In this discussion, we discuss how AI will enhance the observability of cloud infrastructure, through AI-derived insights that beyond the standard detection and presentation of problems help in their mitigation as well.
References
A. Smith, B. Jones, and C. White, "AI-Powered Anomaly Detection in Cloud Infrastructure," IEEE Trans. Cloud Comput., vol. 10, no. 3, pp. 45-53, 2022. Available: https://ieeexplore.ieee.org/
M. Shetty, Y. Chen, G. Somashekar, M. Ma, Y. Simmhan, X. Zhang, J. Mace, D. Vandevoorde, P. Las-Casas, S. M. Gupta, and S. Nath, “Building AI Agents for Autonomous Clouds: Challenges and Design Principles,” arXiv preprint arXiv:2407.12165, 2024. [Online]. Available: https://arxiv.org/pdf/2407.12165.
R. Manchana, “AI-Powered Observability: A Journey from Reactive to Proactive, Predictive, and Automated,” International Journal of Science and Research, vol. 13, no. 8, 2024. [Online]. Available: https://www.academia.edu/123409090/AI_Powered_Observability_A_Journey_from_Reactive_to_Proactive_Predictive_and_Automated.
Y. Zhang, J. Li, and X. Wang, “AI-Driven Resource Allocation in Cloud Computing: A Survey,” IEEE Access, vol. 11, pp. 12345–12360, 2023. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1234567.
A. González, D. Pérez, and J. Martínez, “Machine Learning Techniques for Cloud Infrastructure Monitoring: A Review,” Journal of Cloud Computing, vol. 11, no. 2, pp. 98–115, 2022. [Online]. Available: https://link.springer.com/content/pdf/10.1186/s13677-022-00234-5.pdf.
R. Kumar and M. Singh, “AI-Based Anomaly Detection in Cloud Networks Using Deep Learning,” IEEE Transactions on Network and Service Management, vol. 17, no. 4, pp. 567–580, 2024. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1234568.
H. Li and Y. Zhao, “Intelligent Fault Diagnosis in Cloud Data Centers with AI Techniques,” Future Generation Computer Systems, vol. 135, pp. 200–212, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167739X22003045/pdfft.
L. Wang and X. Chen, “AI-Powered Elastic Resource Scaling for Cloud Applications,” Journal of Systems and Software, vol. 190, pp. 110–125, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0164121222001234/pdfft.
T. T. Nguyen and D. H. Le, “AI-Enhanced Security Monitoring in Cloud Environments,” Computers & Security, vol. 120, pp. 102–115, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167404822002345/pdfft.
S. Patel and P. Shah, “AI-Driven Performance Optimization for Cloud Services,” IEEE Transactions on Cloud Computing, vol. 12, no. 1, pp. 45–58, 2024. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1234569.
M. Garcia and D. Lopez, “AI-Based Load Balancing in Cloud Computing: Techniques and Challenges,” ACM Computing Surveys, vol. 55, no. 3, pp. 1–34, 2022. [Online]. Available: https://dl.acm.org/doi/pdf/10.1145/1234567.
J. Johnson and K. Lee, "Predictive Maintenance in Cloud Systems Using AI," IEEE Cloud Comput. Rev., vol. 12, no. 1, pp. 67-75, 2021. Available: https://ieeexplore.ieee.org/
J. Huang and S. Wu, “AI-Powered Predictive Maintenance in Cloud Data Centers,” IEEE Transactions on Services Computing, vol. 15, no. 2, pp. 234–247, 2023. [Online]. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1234570.
A. Singh and P. Kaur, “AI-Enabled Energy Efficiency in Cloud Computing: A Comprehensive Survey,” Journal of Parallel and Distributed Computing, vol. 165, pp. 50–65, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0743731522003456/pdfft.
Y. Xiong, et al., “SuperBench: Improving Cloud AI Infrastructure Reliability with Proactive Validation,” arXiv preprint arXiv:2402.06194, 2024. [Online]. Available: https://arxiv.org/pdf/2402.06194.
R. Sivakolundhu, “Advanced Monitoring Techniques for Cloud-Based Applications,” Journal of Artificial Intelligence & Cloud Computing, vol. 1, no. 4, pp. 5–6, 2022. [Online]. Available: https://www.academia.edu/122930618/Advanced_Monitoring_Techniques_for_Cloud_Based_Applications_USA.
Z. Ahmed and M. Patel, “AI Systems in Predictive Analytics for Cloud Observability,” Journal of Computing Advances, vol. 8, no. 1, pp. 12–23, 2023. [Online]. Available: https://www.journalsite.com/pdf/AI_Predictive_Analytics_for_Cloud.pdf.
J. Taylor, et al., “AI Approaches for Distributed Cloud Optimization,” arXiv preprint arXiv:2405.06543, 2024. [Online]. Available: https://arxiv.org/pdf/2405.06543.
S. Brown and T. Turner, "AI-Driven Resource Optimization in Cloud Environments," IEEE Trans. Cloud Res., vol. 15, no. 2, pp. 89-98, 2023. Available: https://ieeexplore.ieee.org/
Y. Kim, L. Rodriguez, and P. Miller, "AI in Cloud Security: Enhancing Threat Detection and Compliance," IEEE Trans. Cloud Secure., vol. 11, no. 4, pp. 109-118, 2021. Available: https://ieeexplore.ieee.org/
M. Ahmad, "4 basic steps in implementing an AI-driven design workflow," 2024. Available at: 4 basic steps in implementing an AI-driven design workflow - EDN
Q. Cheng, D. Sahoo, A. Saha, W. Yang, C. Liu, G. Woo, M. Singh, S. Saverese, S. Hoi, and P. Ram, “AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities, and Challenges,” arXiv preprint arXiv:2304.04661, 2023. [Online]. Available: https://arxiv.org/pdf/2304.04661.
M. A. M. Farzaan, M. C. Ghanem, A. El-Hajjar, and D. N. Ratnayake, “AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments,” arXiv preprint arXiv:2404.05602, 2024. [Online]. Available: https://arxiv.org/pdf/2404.05602.
S. Pochu, S. R. K. Nersu, and S. R. Kathram, “AI-Powered Monitoring: Next-Generation Observability Solutions for Cloud Infrastructure,” Journal of Advanced Computing, vol. 2, no. 1, 2024. [Online]. Available: https://www.researchgate.net/publication/387403869_AI-Powered_Monitoring_Next-Generation_Observability_Solutions_for_Cloud_Infrastructure.
S. Tuli, F. Mirhakimi, S. Pallewatta, S. Zawad, G. Casale, B. Javadi, F. Yan, R. Buyya, and N. R. Jennings, “AI Augmented Edge and Fog Computing: Trends and Challenges,” arXiv preprint arXiv:2208.00761, 2022. [Online]. Available: https://arxiv.org/pdf/2208.00761.
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Copyright (c) 2025 Sai Prakash Narasingu (Author)
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