Autonomous Cloud Engineering Mechanism for Elastic Service Optimization in AI Integrated Enterprise Platforms

Jeyakumar Ramachandran Department of Computer Science and Engineering, HCLTech, Georgia, United States of America.

Abstract: This research focuses on the design and deployment of an Autonomous Cloud Engineering Mechanism (ACEM) to manage the dynamic computational load of enterprise systems integrated with AI. While manual scaling approaches have typically proven inadequate in today’s corporate landscape, which increasingly relies on large-scale machine learning models and near-real-time data processing, they are gradually giving way. This paper presents an intelligent orchestration framework that optimizes the elastic services using predictive analytics and reinforcement learning. The mechanism ensures maximum throughput, minimum latency, and minimum operational cost by dynamically allocating resources in advance. The study employs a large dataset of 351 instances collected from a simulated multi-cloud enterprise environment over a fiscal quarter. Advanced simulation models, such as CloudSim for modeling infrastructure and TensorFlow for the predictive engine, were used to conduct the research. The results show that the ACEM framework is highly efficient compared to reactive scaling protocols, achieving a 29% reduction in resource usage. In this paper, the authors propose a framework for creating self-healing and self-optimizing cloud environments that meet the demanding requirements of next-generation artificial intelligence applications in an unsupervised manner.

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Keywords: Autonomous Cloud Engineering; Elastic Resource Scaling; Enterprise Platform; Cloud Optimization; Predictive Orchestration; Self-Healing Systems; Intelligent Orchestration; Cloud Environments.

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Received on: 20/06/2025Revised on: 09/08/2025Accepted on: 04/09/2025Published on: 01/03/2026

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