Using AI for Real-Time Cloud-Based System Monitoring
DOI:
https://doi.org/10.36676/j.sust.sol.v1.i4.51Keywords:
AI, cloud monitoring, anomalous behaviour detection, predictive analysisAbstract
This paper aims to discuss the application of the real-time cloud-based system-monitoring system with the help of artificial intelligence while describing its advantages, limitations, and possible developments. Due to the increase in cloud solution sophistication, AI solutions provide approaches for a massive co-scale scalability and the capacity to discover and dodge severe issues efficiently while improving overall efficiency. The critical capabilities of AI are, for example, anomaly detection, predictive analytics and automatic response. However, there are some issues that include; data availability issues, model bias issues, and the problems associated with model integration in order to support deployment of the AI ML. The paper explores prospects of AI in the monitoring of clouds with focus on; enhanced prediction mechanisms, edge computing and explainable AI.
References
Kanth, T. C. (2024). AI-POWERED THREAT INTELLIGENCE FOR PROACTIVE SECURITY MONITORING IN CLOUD INFRASTRUCTURES. https://philpapers.org/rec/CHAATI-6
Munagandla, V. B., Dandyala, S. S. V., & Vadde, B. C. (2024). AI-powered cloud-based epidemic surveillance system: A framework for early detection. Revista de Inteligencia Artificial en Medicina, 15(1), 673-690. http://redcrevistas.com/index.php/Revista/article/view/176
Panduman, Y. Y. F., Funabiki, N., Fajrianti, E. D., Fang, S., & Sukaridhoto, S. (2024). A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform. Information, 15(3), 153. https://doi.org/10.3390/info15030153 DOI: https://doi.org/10.3390/info15030153
Villegas-Ch, W., García-Ortiz, J., & Sánchez-Viteri, S. (2024). Towards Intelligent Monitoring in IoT: AI Applications for Real-Time Analysis and Prediction. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3376707 DOI: https://doi.org/10.1109/ACCESS.2024.3376707
Chavan, P., & Chavan, P. (2024, June). Automation of AD-OHC Dashbord and Monitoring of Cloud Resources using Genrative AI to Reduce Costing and Enhance Performance. In 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET) (pp. 1-9). IEEE. https://doi.org/10.1109/ICICET59348.2024.10616299 DOI: https://doi.org/10.1109/ICICET59348.2024.10616299
Rehan, H. (2024). AI-Driven Cloud Security: The Future of Safeguarding Sensitive Data in the Digital Age. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 1(1), 132-151. https://ojs.boulibrary.com/index.php/JAIGS/article/view/89 DOI: https://doi.org/10.60087/jaigs.v1i1.89
Sanodia, G. (2024). Leverage AI to Improve Cloud Transformation. Journal of Scientific and Engineering Research, 11(8), 95-105. https://www.researchgate.net/profile/Geetesh-Sanodia-2/publication/383978285_Leverage_AI_to_Improve_Cloud_Transformation/links/66e2e967b1606e24c224dec6/Leverage-AI-to-Improve-Cloud-Transformation.pdf
Srinivas, P., Husain, F., Parayil, A., Choure, A., Bansal, C., & Rajmohan, S. (2024, April). Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach. In Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (pp. 381-391). https://doi.org/10.1109/TCSET64720.2024.10755518 DOI: https://doi.org/10.1145/3639477.3639753
Olabanji, S. O., Marquis, Y., Adigwe, C. S., Ajayi, S. A., Oladoyinbo, T. O., & Olaniyi, O. O. (2024). AI-driven cloud security: Examining the impact of user behavior analysis on threat detection. Asian Journal of Research in Computer Science, 17(3), 57-74. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4709384 DOI: https://doi.org/10.9734/ajrcos/2024/v17i3424
Stutz, D., de Assis, J. T., Laghari, A. A., Khan, A. A., Andreopoulos, N., Terziev, A., ... & Grata, E. G. (2024). Enhancing Security in Cloud Computing Using Artificial Intelligence (AI). Applying Artificial Intelligence in Cybersecurity Analytics and Cyber Threat Detection, 179-220. https://doi.org/10.1002/9781394196470.ch11 DOI: https://doi.org/10.1002/9781394196470.ch11
Rahaman, M., Lin, C. Y., Pappachan, P., Gupta, B. B., & Hsu, C. H. (2024). Privacy-centric AI and IoT solutions for smart rural farm monitoring and control. Sensors, 24(13), 4157. https://doi.org/10.3390/s24134157 DOI: https://doi.org/10.3390/s24134157
Chanthati, S. R. (2024). Artificial Intelligence-Based Cloud Planning and Migration to Cut the Cost of Cloud Sasibhushan Rao Chanthati. American Journal of Smart Technology and Solutions, 3(2), 13-24. https://www.researchgate.net/profile/Sasibhushan-Rao-Chanthati/publication/382952222_Artificial_Intelligence-Based_Cloud_Planning_and_Migration_to_Cut_the_Cost_of_Cloud_Sasibhushan_Rao_Chanthati/links/66bcfc2e2ff54d6c9ed0b287/Artificial-Intelligence-Based-Cloud-Planning-and-Migration-to-Cut-the-Cost-of-Cloud-Sasibhushan-Rao-Chanthati.pdf DOI: https://doi.org/10.54536/ajsts.v3i2.3210
Jones, R. (2024). The Impact of AI on Secure Cloud Computing: Opportunities and Challenges. The Indonesian Journal of Computer Science, 13(4). https://doi.org/10.33022/ijcs.v13i4.4383 DOI: https://doi.org/10.33022/ijcs.v13i4.4383
Barua, B., & Kaiser, M. S. (2024). AI-Driven Resource Allocation Framework for Microservices in Hybrid Cloud Platforms. arXiv preprint arXiv:2412.02610. https://doi.org/10.48550/arXiv.2412.02610
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