Autonomous Enterprise Operations Using Reinforcement Learning and Cloud Native Technologies Frameworks
Abstract
The rapid evolution of cloud computing, microservices, containers, and intelligent automation has created new opportunities for organizations to develop autonomous enterprise operations. Traditional enterprise operations depend heavily on manual administration, predefined rules, static resource allocation, and human intervention for incident management and infrastructure optimization. These approaches become increasingly difficult to maintain as enterprise systems grow across distributed cloud-native environments. Reinforcement learning (RL) offers an alternative approach by enabling intelligent agents to learn optimal operational policies through interaction with dynamic environments and feedback from their actions. When integrated with cloud-native technologies such as containers, Kubernetes, microservices, service meshes, observability platforms, serverless computing, and continuous integration and continuous delivery pipelines, reinforcement learning can support adaptive and autonomous operational management. This research examines a framework for autonomous enterprise operations using reinforcement learning and cloud-native technology frameworks. The proposed approach combines real-time telemetry, state representation, RL-based decision-making, policy evaluation, automated execution, and feedback mechanisms within a cloud-native architecture. Potential applications include automated resource scaling, workload scheduling, service placement, anomaly response, cost optimization, capacity management, and self-healing infrastructure. The research methodology incorporates literature analysis, conceptual framework development, scenario-based experimentation, and quantitative evaluation. Performance is assessed using availability, resource utilization, response time, cost, recovery time, policy effectiveness, and automation levels. The study also investigates challenges involving exploration risks, model instability, security, observability, explainability, computational overhead, and governance. The research argues that carefully bounded reinforcement learning can contribute to resilient, adaptive, and efficient autonomous enterprise operations when integrated with strong policy controls, monitoring, verification, and human oversight.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
15340-15349 |
Published |
December 17, 2024 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Karthik Durai (2024). Autonomous Enterprise Operations Using Reinforcement Learning and Cloud Native Technologies Frameworks. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 7 No. 6 (2024): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 15340-15349. https://doi.org/10.15662/IJAESIT.2024.0706008 |
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