Transformer-Based Multi-Signal Predictive Autoscaling for SLA-Aware Resource Management in Kubernetes-Orchestrated Cloud-Native Environments

Abstract

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.