AI-Powered Optimization of Non-Production Environments: Turning Constraints into Business Value
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Abstract
Non-production systems like development, testing, and staging environments are necessary to deliver software, but they are frequently characterized by poor resource usage, conflicts of the environments, and sluggish provisioning. The paper introduces a machine-intelligence based optimization system to improve the effectiveness and stability of non-production environments. The suggested solution uses Hybrid AI-Based Predictive Optimization with Kubernetes for smart resource management and automated environment control. The system examines the history of workload and dynamically distributes computing capacity in order to minimize the idle capacity and enhance the effectiveness of the scheduling. Kubernetes provides functionality to orchestrate containers automatically, spin up environments quickly and create infrastructure which is scalable. The experimental and simulation findings show that there are great improvements in resource utilization, system availability and speed of deployment as compared to the traditional and traditional approach of managing the environment using the statical and rule-based methods. The proposed framework is expected to reduce operational cost and enhance the efficiency of CI/CD pipeline implementation. Overall, the research demonstrates that AI-based optimization has the potential to convert constrained non-production environments into scalable infrastructure systems that drive business value
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