A Real-Time Workload Monitoring–Based Intelligent Auto-Scaling Framework for Cloud Systems
Year: 2026
Type: conference-paper
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.
Authors: Nikita Singh, Meenu Gupta, Rakesh Kumar, Aayush Garg
Institution: Chandigarh University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203856044