Dynamic Autoselection and Autotuning of Machine Learning Models for Cloud Network Analytics

Modern cloud computing environments require intelligent resource management systems that can dynamically adapt to varying workloads while maintaining service quality guarantees. Traditional approaches to virtual machine allocation focus primarily on resource availability, often neglecting critical response time requirements essential for Service Level Agreement compliance. This research introduces EELBRAM (Energy Efficient Load Balancing and Resource Allocation Method), a comprehensive framework that integrates machine learning algorithms with intelligent task scheduling mechanisms to address both resource optimization and response time constraints simultaneously. Our methodology employs Support Vector Machine algorithms for predictive resource allocation, combined with energy-aware load balancing techniques to minimize power consumption while maximizing performance. The system incorporates a multi-objective fitness function that balances SLA satisfaction metrics through weighted optimization strategies. Experimental validation demonstrates superior performance in resource utilization efficiency, achieving 18% improvement in response time prediction accuracy and 23% reduction in energy consumption compared to conventional allocation methods. The proposed framework addresses scalability challenges in dynamic cloud environments while maintaining operational cost effectiveness for service providers.