Predictive Autoscaling in Cloud-Native and Federated Cloud-Edge Computing Environments: A Taxonomy and Future Directions

2026-06-05

AI Summary

Traditional threshold-driven reactive autoscaling struggles to handle dynamic workloads, heterogeneous environments, and latency-sensitive applications, often leading to resource imbalance and performance degradation. This work proposes a predictive autoscaling framework that integrates drift awareness, uncertainty quantification, and privacy-preserving mechanisms, enabling proactive and adaptive scheduling in cloud-edge协同 environments through Kubernetes Custom Resource Definitions (CRDs) and a MAPE (Monitor-Analyze-Plan-Execute) control loop. The core contributions include a comprehensive taxonomy encompassing triggering mechanisms, target entities, prediction models, and evaluation metrics; the formulation of an Autoscaling Drift Index (ADI); and the integration of federated learning, container isolation, and feedback correction techniques. Together, these advances establish a theoretical foundation and key technical pathways for autoscaling in cloud-native and cloud-edge federated systems.

Abstract

Autoscaling is a key capability in cloud-native systems, where dynamic workloads, heterogeneous environments, and latency-sensitive applications require efficient and adaptive resource management. Traditional reactive approaches based on fixed thresholds often respond too late, leading to resource imbalance, performance degradation, and unstable scaling behavior. Recent advances in predictive models, Kubernetes Custom Resource Definitions (CRDs), Monitor-Analyse-Plan-Execute (MAPE) based control loops, and federated learning (FL) have enabled more proactive and autonomous autoscaling strategies. This paper presents a structured review of these developments. It first introduces a taxonomy of autoscaling techniques based on triggers, targets, prediction models, and evaluation metrics. It then examines predictive autoscaling approaches and CRD-based mechanisms, including Kubernetes operators and reconciliation workflows. Further, it analyses autoscaling in federated learning environments, highlighting reactive and proactive strategies alongside privacy-preserving techniques and container-level isolation. The paper also discusses drift-aware and uncertainty-aware autoscaling, incorporating concepts such as the Autoscaling Drift Index (ADI), feedback-driven correction, and stability control for heterogeneous workloads. Finally, it outlines open challenges and future research directions, providing a foundation for next-generation intelligent predictive autoscaling in cloud-edge environments.

Problem

• predictive autoscaling
• cloud-native
• federated cloud-edge computing
• resource management
• latency-sensitive applications

Innovation

• predictive autoscaling
• Kubernetes CRDs
• federated learning
• drift-aware scaling
• MAPE loop