Adaptive Resource Management in Cloud-Native Architectures Using Predictive Analytics and Reinforcement Learning Techniques

Abstract

Cloud-native systems increasingly confront volatile workloads, tight SLOs, and rising cost/energy pressures. This paper proposes an adaptive resource management framework that fuses predictive analytics with reinforcement learning (RL) to optimize autoscaling and scheduling across Kubernetes-based microservices. First, multivariate forecasting models (e.g., LSTM/Temporal-Fusion variants with seasonality regressors) anticipate short-horizon demand, latency, and queue depth using traces and metrics exported via OpenTelemetry. These forecasts parameterize a constrained Markov decision process in which an RL agent (PPO with safe-exploration and cost penalties) learns scaling and placement policies that jointly minimize p95 latency, cloud spend, and energy while meeting per-service SLOs and anti-affinity constraints. To ensure safe rollouts, we employ a digital-twin simulator calibrated from production telemetry for off-policy evaluation, drift detectors for online model recalibration, and canary gating for incremental policy activation. The orchestration layer integrates with HPA/VPA/KEDA, node pools, and spot/On-Demand mixes; actions include replica counts, CPU/memory limits, pod scheduling hints, and serverless concurrency caps. Across mixed OLTP/OLAP traces and bursty event streams, the framework yields consistent gains over threshold-based and purely predictive baselines, reducing SLO violations and cost without sacrificing stability. We discuss explainability (SHAP-based action attributions), carbon-aware placement, and failure-mode containment, and outline an MLOps/AIOps pathway for continuous validation. The result is a pragmatic blueprint to operationalize learning-augmented autoscaling in production, bridging accuracy of demand prediction with the adaptability of RL control