Adaptive Autoscaling Using Workload Forecasting

Cloud computing environments must continuously adapt to fluctuating workload demands while maintaining service reliability and operational efficiency. Traditional static and reactive resource allocation approaches often struggle to respond effectively to dynamic workload patterns, leading to resource inefficiencies and performance degradation. This paper presents a predictive and adaptive resource management framework that combines workload forecasting with policy-driven orchestration to enable proactive scaling across distributed cloud infrastructures. The proposed architecture integrates predictive analytics with automated resource allocation mechanisms to anticipate demand variations and adjust computing resources before service quality is affected. Evaluation using realistic multi-tenant cloud workload traces demonstrates improvements in resource utilization, application responsiveness, and provisioning efficiency when compared with conventional reactive autoscaling techniques. The framework also maintains high service availability during infrastructure disruptions and exhibits strong scalability across distributed environments. Comparative analysis against widely adopted cloud scaling approaches highlights the advantages of prediction-driven orchestration for modern cloud platforms. The findings suggest that proactive resource management can enhance cloud operational efficiency while supporting reliable service delivery in large-scale environments. Future work will investigate reinforcement learning-based optimization and sustainability-aware resource placement strategies.