A Hybrid Machine Learning and Control-Theoretic Framework for Stability-Assured Resource Management in Large-Scale Cloud Computing Environments

Abstract

Large-scale cloud computing environments must continuously allocate, scale, and reconfigure resources under uncertain demand, multi-tenant interference, heterogeneous infrastructure, and stringent service-level objectives. Conventional threshold-based autoscaling remains widely used because of its operational simplicity, yet it often reacts after performance degradation has already occurred. Purely machine-learning-driven methods can improve prediction and adaptation, but they may produce unsafe actions when exposed to distribution shifts, delayed actuation, noisy telemetry, or unobserved dependencies. This paper proposes a hybrid machine learning and control-theoretic framework for stability-assured resource management in large-scale cloud computing environm ents. The framework integrates workload forecasting, online quality-of-service modeling, constrained optimization, feedback control, Lyapunov-style stability reasoning, and policy-governed decision intelligence. The proposed design separates predictive intelligence from safety-critical actuation: machine learning estimates near-future demand, performance sensitivity, and workload classes, while a constrained model-predictive controller and supervisory stability guard transform those estimates into resource actions that respect service-level, cost, and stability constraints. The framework is formulated for containerized and virtualized cloud platforms, including horizontal scaling, vertical resource adjustment, admission control, and workload placement. It defines a conceptual architecture, analytical stability conditions, evaluation metrics, and deployment implications for cloud operators. The analytical discussion shows that a hybrid design can reduce elastic lag, control oscillatory scaling behavior, preserve bounded latency error, and support auditable resource governance more effectively than purely reactive autoscaling or unconstrained learning policies. The paper contributes a structured research model for stability-aware cloud resource management and identifies future directions in safe reinforcement learning, distributed control, explainable autoscaling, and production-grade validation.