A Real-Time Workload Monitoring–Based Intelligent Auto-Scaling Framework for Cloud Systems

Abstract

Kubernetes Horizontal Pod Autoscaler(HPA) and other existing auto-scaling solutions that respond reactively to demand experience significant delays in provisioning and inefficiencies when responding to sudden workload spikes. This paper proposes a new Real-Time Workload Monitoring-Based Intelligent Auto-Scaling Framework that uses a hybrid time-series forecasting model to proactively manage cloud computing resources. Specifically, it combines periodicity detection with Prophet-based models and long-term dependence capture via Long Short-Term Memory (LSTM) networks to provide advanced notice of CPU and memory requirements before load surges occur. The results of this approach were compared against traditional reactive auto-scaling methods and demonstrated that it significantly reduced scaling latencies, minimized service level agreement (SLA) violations, and optimized resource utilization during load surges.