Redefining “Infrastructure as Code” Orchestration Using AI

Authors: Georges Bou Ghantous

Abstract:

This research delves into the transformative impact of Artificial Intelligence (AI) on Infrastructure as Code (IaC) practices, specifically focusing on the redefinition of infrastructure orchestration. By harnessing AI technologies such as machine learning algorithms and predictive analytics, organizations can achieve unprecedented levels of efficiency and optimization in managing their infrastructure resources. AI-driven IaC introduces proactive decision-making through predictive insights, enabling organizations to anticipate and address potential issues before they arise. Dynamic resource scaling, facilitated by AI, ensures that infrastructure resources can seamlessly adapt to fluctuating workloads and changing business requirements. Through case studies and best practices, this paper sheds light on the tangible benefits and challenges associated with AI-driven IaC transformation, providing valuable insights for organizations navigating the evolving landscape of digital infrastructure management.

Keywords: Artificial intelligence, AI, infrastructure as code, IaC, efficiency optimization, predictive insights, dynamic resource scaling, proactive decision-making.

References:

[1] Hladik, S., Sengupta, P. and Shanahan, M.-C. (2023) ‘Museum Facilitator Practice as Infrastructure Design Work for Public Computing’, Cognition and Instruction, 41(2), pp. 248–289. doi: 10.1080/07370008.2022.2129639.
[2] Kumar, M., Mishra, S., Lathar, N.K. and Singh, P., 2023. Infrastructure as code (IAC): insights on various platforms. In Sentiment Analysis and Deep Learning: Proceedings of ICSADL 2022 (pp. 439-449). Singapore: Springer Nature Singapore.
[3] Berman, G., 2023, August. Machine Learning practices and infrastructures. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (pp. 466-481).
[4] Sokolowski, D., Spielmann, D. and Salvaneschi, G., 2024. The PIPr Dataset of Public Infrastructure as Code Programs. IEEE/ACM MSR. Accepted.
[5] Srivatsa, K.G., Mukhopadhyay, S., Katrapati, G. and Shrivastava, M., 2024. A Survey of using Large Language Models for Generating Infrastructure as Code. arXiv preprint arXiv:2404.00227.
[6] Code, A.I.A., Dynamic Autonomic Systems: Augmenting Infrastructure as Code with Machine Learning for Proactive and Predictive Scaling in Complex IT Environments. Journal ID, 9339, p.1263.
[7] Dunay, O., Cheng, D., Tait, A., Thakkar, P., Rigby, P.C., Chiu, A., Ahmad, I., Ganesan, A., Maddila, C., Murali, V. and Tayyebi, A., 2024. Multi-line AI-assisted Code Authoring. arXiv preprint arXiv:2402.04141.
[8] Murali, V., Maddila, C., Ahmad, I., Bolin, M., Cheng, D., Ghorbani, N., Fernandez, R. and Nagappan, N., 2023. CodeCompose: A large-scale industrial deployment of AI-assisted code authoring. arXiv preprint arXiv:2305.12050.
[9] Idrisov, B. and Schlippe, T., 2024. Program Code Generation with Generative AIs. Algorithms, 17(2), p.62.
[10] Pujar, S., Buratti, L., Guo, X., Dupuis, N., Lewis, B., Suneja, S., Sood, A., Nalawade, G., Jones, M., Morari, A. and Puri, R., 2023, July. Automated code generation for information technology tasks in yaml through large language models. In 2023 60th ACM/IEEE Design Automation Conference (DAC) (pp. 1-4). IEEE.
[11] Mungoli, N., 2023. Scalable, Distributed AI Frameworks: Leveraging Cloud Computing for Enhanced Deep Learning Performance and Efficiency. arXiv preprint arXiv:2304.13738.
[12] Umoga, U.J., Sodiya, E.O., Ugwuanyi, E.D., Jacks, B.S., Lottu, O.A., Daraojimba, O.D. and Obaigbena, A., 2024. Exploring the potential of AI-driven optimization in enhancing network performance and efficiency. Magna Scientia Advanced Research and Reviews, 10(1), pp.368-378.
[13] Ylitalo, A., 2024. AI assisted public cloud management.
[14] Jensen, A., 2024. AI-Driven DevOps: Enhancing Automation with Machine Learning in AWS. Integrated Journal of Science and Technology, 1(2).
[15] Kaggwa, S., Eleogu, T.F., Okonkwo, F., Farayola, O.A., Uwaoma, P.U. and Akinoso, A., 2024. AI in decision making: transforming business strategies. International Journal of Research and Scientific Innovation, 10(12), pp.423-444.
[16] Rehan, H., 2024. Revolutionizing America's Cloud Computing the Pivotal Role of AI in Driving Innovation and Security. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 2(1), pp.239-240.
[17] George, J.G., 2024, Transforming Banking in the Digital Age: The Strategic Integration of Large Language Models and Multi-Cloud Environments.
[18] Balakrishna, Balasubrahmanya, Narayana Challa, Sridhar Mooghala, and Praveen Kumar Tammana. "Synergizing Digital Transformation." (2024).
[19] Prakash, M., 2024. Role of Generative AI tools (GAITs) in Software Development Life Cycle (SDLC)-Waterfall Model (Doctoral dissertation, Massachusetts Institute of Technology).
[20] Alonso, J., Orue-Echevarria, L., Osaba, E., López Lobo, J., Martinez, I., Diaz de Arcaya, J. and Etxaniz, I., 2021. Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum. Information, 12(8), p.308.
[21] ARAa, J. and Cecilia, S.L., 2022. Artificial intelligence in web accessibility: potentials and possible challenges. Proceedings of IAC, p.173.
[22] Alonso, J., Piliszek, R. and Cankar, M., 2022. Embracing IaC through the DevSecOps philosophy: Concepts, challenges, and a reference framework. IEEE Software, 40(1), pp.56-62.
[23] Markolf, Samuel & Chester, Mikhail & Allenby, Braden. (2021). Opportunities and Challenges for Artificial Intelligence Applications in Infrastructure Management During the Anthropocene. Frontiers in Water. 2. 551598. 10.3389/frwa.2020.551598.