Reinforcement Learning for Dynamic Cloud Resource BI Optimization
Abstract
Dynamic cloud environments: These environments have a varying volume of data, query workload, number of users and/or business continuity objectives (SLOs) for Business Intelligence (BI) workloads. Due to these uncertainties, it will be impossible to balance the three important factors of performance, energy efficiency and cost with fixed rule and threshold based autoscaling approaches. In this research article we want to propose a framework for dynamically optimizing cloud resource BI based on Reinforcement Learning (RL) model who will continually interact with the cloud environment and learn a set of optimal policies for cloud resource allocation. This framework consists of five layers such as BI workload monitoring, state space construction, RL decision engine, resource orchestration and evaluation of performance feedback. Every system metric (such as CPU usage, memory usage, query response time, queue length, storage I/O, workload priority and cost constraints) is important. Those can be the scaling of virtual machines, moving containers around and changing memory sizes, prioritizing BI queries, moving workloads from node to node, etc., and are decided by the RL agent. The rewards can be specified as a multi-objective function, which include reduction of response time, reduction of cloud cost, resource utilization gain and SLA and energy consumption satisfaction. The proposed model is adaptive – it adapts itself based on the trends of the historical data or real-time data of BI workload. Algorithms like Q-learning, Deep Q-Networks and Proximal Policy Optimization could be potentially applied for an intelligent decision making in cloud based BI platforms, the study mentions. This framework can enhance the analytical responsiveness, minimise resources wastage and enable cost-aware business decision systems. It also helps businesses in planning for capacity with a pro-active assessment and hand over their real-time dashboards and predictive analytics services. Lastly, reinforcement learning can be leveraged for scaling and adapting for optimal BI workloads in a complex, dynamic and data rich cloud environment.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
68-76 |
Published |
February 18, 2026 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Aditi Namdeo (%2026). Reinforcement Learning for Dynamic Cloud Resource BI Optimization. International Journal of Science, Research and Technology , Vol. 9 No. 1 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 68-76. https://doi.org/10.15662/IJSRAT.2026.0901008 |
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