Optimized Resource Management in Cloud Computing: A Unified Approach with Adaptive Allocation and Predictive Scaling

Harish Narne

Abstract: Efficient resource management in cloud computing is crucial due to the dynamic nature of workloads and unpredictable demand patterns, which can result in resource wastage, degraded performance, and SLA violations. This paper presents an advanced resource allocation framework integrating adaptive workload distribution with predictive scaling algorithms. The adaptive component dynamically reallocates resources based on real-time metrics, ensuring balanced utilization. At the same time, the predictive scaling algorithm leverages machine learning models, such as recurrent neural networks (RNNs), to forecast future workload demands and enable proactive scaling decisions. Experimental evaluations in a simulated environment demonstrated significant improvements, including a 25% increase in resource utilization, a 20% reduction in response time, and a 25% decrease in operational costs compared to static and dynamic methods. These findings underscore the transformative potential of the proposed system to enhance scalability, performance, and cost-efficiency in modern cloud environments.

Keywords: Cloud Computing, Resource Management, Predictive Scaling, Adaptive Workload Distribution, Machine Learning, Cost Optimization, SLA Compliance