Frameworks For Implementing AI-Driven Cloud Orchestration

Аннотация

This article presents an analysis of frameworks OPEN ACCESS designed for AI-driven orchestration of cloud resources, SUBMITED 26 April 2025 focusing on contemporary methods and architectural ACCEPTED 18 May 2025 models aimed at improving the efficiency, adaptability, PUBLISHED 10 June 2025 and energy performance of cloud computing VOLUME Vol.10 Issue 06 2025 environments. The study includes a comprehensive review of applied machine learning techniques, deep CITATION learning, reinforcement learning algorithms, Prashant Dathwal. (2025). Frameworks for implementing AI-driven cloud evolutionary algorithms, and hybrid approaches used orchestration. The American Journal of Engineering and Technology, for workload prediction, resource allocation 7(06), 81–87. https://doi.org/10.37547/tajet/Volume07Issue06-08. optimization, and autonomous decision-making. The COPYRIGHT paper identifies key integration challenges, © 2025 Original content from this work may be used under the terms computational overhead, issues of interpretability and of the creative commons attributes 4.0 License. security, and outlines development prospects through the implementation of Explainable AI and standardized modular architectures. The findings demonstrate the potential of the proposed approaches for practical implementation in dynamic cloud infrastructures. The insights provided in this article will be of interest to researchers and professionals working in the fields of distributed computing, cloud technologies, and artificial intelligence, as it analyzes modern frameworks designed to build efficient coordination systems within hybrid computing environments. Moreover, the material will be useful for specialists and academics seeking to integrate cutting-edge technological solutions into corporate and research projects, enabling optimized data processing and enhanced adaptability of information systems in an era of continuous digital transformation. Keywords: cloud computing; orchestration; artificial intelligence; machine learning; deep learning; reinforcement learning; evolutionary algorithms; optimization; predictive analytics; hybrid methods. Introduction: Cloud computing has become an integral part of modern IT infrastructure, offering scalability, flexibility, and cost-efficiency. However, as the volume The American Journal of Engineering and Technology 81 https://www.theamericanjournals.com/index.php/tajet and complexity of cloud environments grow, traditional driven orchestration methods for cloud resources and resource management methods increasingly struggle automation, as well as broader aspects involving the with challenges such as dynamic workloads, the need for integration of intelligent algorithms into industrial, IoT, efficient resource balancing, and maintaining high and security systems. However, certain contradictions service quality. In this context, the application of persist: while some authors emphasize the advantages artificial intelligence (AI) methods to optimize resource of automation and system adaptability enabled by AI, management and automate orchestration processes has others focus on the associated risks, particularly become particularly relevant [1, 2]. regarding data security and the opacity of decision- making algorithms. Moreover, the integration of AI to The existing literature demonstrates a wide range of support strategic business processes remains approaches to integrating AI into the management of underexplored, as does the interdisciplinary adaptation cloud infrastructures and related processes. Studies of these technologies across various economic sectors. focused specifically on cloud orchestration emphasize These gaps highlight the need for further research aimed the importance of intelligent algorithms for resource at aligning theoretical models with the practical distribution optimization and the automation of demands of the digital economy. software deployment. For instance, Selvarajan G. [1] highlights the use of machine learning algorithms for the The aim of this article is to review frameworks used for dynamic adaptation of cloud resources, which enhances implementing AI-based orchestration in cloud infrastructure efficiency. Mallreddy S. R. [2] expands on environments. this by describing methodologies for integrating intelligent automation into software deployment The novelty lies in offering a distinct approach to processes, leading to reduced operational costs and applying frameworks for orchestrating cloud lower system response times. A similar approach is coordination based on artificial intelligence algorithms. found in the work of Selvarajan G. P. [7], where data mining is applied to predict changes in dynamic The author’s hypothesis assumes that integrating environments, supporting informed decision-making various AI methods within a unified orchestration under uncertainty. framework for cloud resources will enhance resource utilization efficiency, reduce operational costs, and