AROF: adaptive resource optimization framework for Kubernetes cluster using workload forecasting
Rajvi Patel, Ashwin Makwana · International Journal of Parallel Emergent and Distributed Systems · 2025
Traditional Kubernetes autoscaling struggles with dynamic workloads, causing SLA violations and inefficiency. We propose AROF: an Adaptive Resource Optimization Framework integrating hybrid workload classification (clustering+tagging), multi-horizon LSTM forecasting, and cost-aware autoscaling with tunable cost-SLA trade-offs. AROF formulates VM provisioning as a constrained optimization problem with quadratic SLA penalties, enabling fine-grained resource management. Extensive evaluation using Alibaba Cloud 2022 traces demonstrates AROF reduces SLA violations by 81% and improves cost efficiency by 22.4% compared to standard Kubernetes autoscalers, while outperforming recent proactive baselines. The framework provides a scalable, interpretable solution for intelligent resource optimization in production Kubernetes environments.