CloudOps and AIOps Automation Frameworks
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I4P114Keywords:
CloudOps, AIOps, Automation Frameworks, Cloud Computing, IT Operations, Machine Learning, Monitoring, Predictive AnalyticsAbstract
Cloud computing has transformed the IT in the enterprise by offering scalability, economy and accessibility at a global scale. However, the distributed architecture, the workloads of containerization and the multi-cloud plans are growing at a rapid pace, which means that the existing IT functions are no longer sufficient to serve the increased size, speed and complexity of the cloud. This has witnessed the invention of Cloud Operations (CloudOps) and Artificial Intelligence to run IT Operations (AIOps). There is a cloudops which is the automation, monitoring, and governing of cloud-native infrastructure and AIOps advanced analytics and machine-learning (ML)/natural-language processing (NLP) anticipating problems in their use of cloud infrastructure to make its use more efficient and, in fact, less human-intensive. The paper will thoroughly analyze CloudOps and AIOps automation structure, principles, architecture, tools, and benefits thereof. A comprehensive review of the literature is conducted on the basis of the analysis of the former research on cloud management automation and the integration of AI-based insights into IT operations. I recommend a hybrid CloudOpsAIOps automation system in the methodology section, which has a layered structure and is used to cover resource orchestration, observability, anomaly detection and intelligent decision-making. The efficiency improvement is experimented with simulation experiments in terms of Mean Time to Detection (MTTD), Mean Time to Resolution (MTTR), cost optimality and reliability benefit of simulation experiment on real-world cloud workload. The findings show that CloudOps using AIOps lessens the down times and improves the system resiliency and supportive predictive and prescribing analytics. The study finds that CloudOps automation and AIOps framework are not only critical change agents of digital transformation, but also essential in sustainable cloud governance
References
1. Chen, Z., Kang, Y., Li, L., Zhang, X., Zhang, H., Xu, H., ... & Lyu, M. R. (2020, November). Towards intelligent incident management: why we need it and how we make it. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1487-1497).
2. Yeruva, A. R., & Ramu, V. B. (2023). AIOps research innovations, performance impact and challenges faced. International Journal of System of Systems Engineering, 13(3), 229-247.
3. Joy, M., Venkataramanan, S., Ahmed, M., Mark, M., Gudala, L., Shaik, M., ... & Reddy Vangoor, V. K. (2024). AIOps in Action: Streamlining IT Operations Through Artificial Intelligence. AIOps in Action: Streamlining IT Operations Through Artificial Intelligence," International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 2175-2185.
4. Vishal Diyora, "AI for Cloud Ops Transformation and Innovation," International Journal of Computer Trends and Technology (IJCTT), vol. 72, no. 4, pp. 140-144, 2024. Crossref, https://doi.org/10.14445/22312803/ IJCTT-V72I4P118
5. Huda, A. N., & Kusumawardani, S. S. (2022). Kubernetes Cluster Management for Cloud Computing Platform: A Systematic Literature Review. JUTI: Jurnal Ilmiah Teknologi Informasi, 75-83.
6. Kyadasu, R. (2024). Exploring Infrastructure as Code Using Terraform in Multi-Cloud Deployments. Available at SSRN 5075647.
7. What is AIOps?, AWS, online. https://aws.amazon.com/what-is/aiops/
8. Slawik, M., Zilci, B. I., Demchenko, Y., Baranda, J. I. A., Branchat, R., Loomis, C., ... & Blanchet, C. (2015, December). CYCLONE unified deployment and management of federated, multi-cloud applications. In 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC) (pp. 453-457). IEEE.
9. AIOps Use for Cloud Operations Automation at scale, medium, 2023. online. https://medium.com/@hello_26308/aiops-use-for-cloud-operations-automation-at-scale-811ed05c7945
10. Dave, D., Sawhney, G., Khut, D., Nawale, S., Aggrawal, P., & Bhavathankar, P. (2023, November). AIOps-Driven enhancement of log anomaly detection in unsupervised scenarios. In 2023 International Conference on Big Data, Knowledge and Control Systems Engineering (BdKCSE) (pp. 1-6). IEEE.
11. Opara, A., Song, Y., Cho, S. J., & Chung, L. (2019, October). Representing multicloud security and privacy policies and detecting potential problems. In International Conference on Service-Oriented Computing (pp. 57-68). Cham: Springer International Publishing.
12. Alonso, J., Orue-Echevarria, L., & Huarte, M. (2022). CloudOps: Towards the operationalization of the cloud continuum: Concepts, challenges and a reference framework. Applied Sciences, 12(9), 4347.
13. Mulongo, N. Y. (2024, October). Key Performance Indicators of Artificial Intelligence For IT Operations (AIOPS). In 2024 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-8). IEEE.
14. Abbas, S. I., & Garg, A. (2024, March). Aiops in devops: Leveraging artificial intelligence for operations and monitoring. In 2024 3rd International Conference on Sentiment Analysis and Deep Learning (ICSADL) (pp. 64-70). IEEE.
15. Dutta, S., Gera, S., Verma, A., & Viswanathan, B. (2012, June). Smartscale: Automatic application scaling in enterprise clouds. In 2012 IEEE Fifth International Conference on Cloud Computing (pp. 221-228). IEEE.
16. MLOps, AIOps and different -Ops frameworks: Overview & Comparison, k21academy, 2024. online. https://k21academy.com/ai-ml/mlops-aiops-and-ops-framewroks/
17. Manvi, S. S., & Shyam, G. K. (2014). Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey. Journal of network and computer applications, 41, 424-440.
18. Sawant, N., & Shah, H. (2013). Big data ingestion and streaming patterns. In Big Data Application Architecture Q & A: A Problem-Solution Approach (pp. 29-42). Berkeley, CA: Apress.
19. ElSahly, O., & Abdelfatah, A. (2022). A systematic review of traffic incident detection algorithms. Sustainability, 14(22), 14859.
20. Alzubaidi, A., Mitra, K., & Solaiman, E. (2023). A blockchain-based SLA monitoring and compliance assessment for IoT ecosystems. Journal of Cloud Computing, 12(1), 50.
