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.
Nilesh Mutyam· International Journal of Eme...· 0 citations
Kubernetes has become the dominant orchestration substrate for cloud-native applications, yet its native autoscaling mechanisms remain primarily reactive, threshold-driven, and limited in their ability to anticipate workload volatility before service-level agreement violations occur. Modern microservice systems exhibit non-linear interactions among request arrival rates, queueing delays, CPU saturation, memory pressure, network variability, pod cold-start latency, and downstream dependency bottlenecks. These characteristics make single-metric autoscaling policies insufficient for latency-sensitive workloads operating under strict service-level objectives. This paper proposes a Transformer-Based Multi-Signal Predictive Autoscaling framework for SLA-aware resource management in Kubernetes-orchestrated cloud-native environments. The proposed framework integrates heterogeneous observability signals, multi-horizon time-series forecasting, uncertainty-aware decision logic, and Kubernetes-native actuation to allocate resources before overload conditions materialize. Unlike conventional Horizontal Pod Autoscaler configurations that respond after resource utilization crosses predefined thresholds, the proposed approach forecasts near-future demand and performance risk using a Transformer encoder architecture designed to learn long-range dependencies, temporal seasonality, burst behavior, and cross-metric interactions. The framework translates predicted workload and latency risk into safe scaling actions through policy constraints that consider replica bounds, cooldown windows, pod readiness delays, cost budgets, and SLA violation probability. The paper develops the conceptual architecture, methodological workflow, evaluation metrics, and analytical discussion necessary for empirical implementation. The study argues that SLA-aware predictive autoscaling should be treated not merely as a forecasting task but as an integrated control problem involving observability quality, model calibration, decision governance, and runtime safety. The proposed model contributes to cloud resource management research by aligning deep temporal learning with Kubernetes operational semantics and by providing a structured pathway toward more reliable, efficient, and self-adaptive cloud-native platforms.
Nilesh Mutyam· International Journal of Art...· 0 citations
