A Predictive Autoscaling Framework for Cloud Deployment Infrastructure Optimization

Abstract

Cloud Computing has played a vital role in handling data storage, access and processing in distributed environments. Cloud Computing delivers scalable servers, databases and storage resources and abstracts the backend infrastructure to the end users. Workloads exhibit higher instability due to the increase in the dependency of cloud platforms. On the course of this dynamic behavior, Cloud systems need to withstand steady performance, control operational costs and consistently meet QOS requirements. Requests drop frequently results in wasted computational capacity. Sudden spikes in network can introduce performance bottlenecks and degrade application responsiveness. We propose a predictive load management system designed to anticipate multiple task's trends and optimize decisions using machine learning and deep learning models. The approach replaces reactive rule-based scaling with demand prediction and enables proactive resource readiness that reduces congestion and resource wastage. The architecture primarily focuses on an adaptable learning pipeline that is enhanced for deployment and integration in AWS services. The performance of the proposed system is evaluated on the basis of continuous load and gradual increases in traffic. Apache JMeter serves as our vital load-balancing testing tool that enables controlled variation of user requests and time intervals. Compared to traditional reactive approaches, our approach demonstrates that predictive autoscaling uplifts the QOS (Quality of Service) performance. It is designed to enhance the agility of cloud resource management and reduces latency.