Cloud-Native Control Plane Design for Modern Network Automation Systems

Abstract

The modern network automation is increasingly demanding cloud-native orchestrated networks capable of dynamically delivered workloads, distributed functions, and heterogeneous networks. However, containerization and orchestration are insufficient to create a reliable network control plane because changes in configuration are long-lived, partially observable, and frequently implemented on devices that do not have a transactional updating semantic. This study proposes a cloud-native control plane that incorporates a declarative API, long-term desired-state storage, autonomous reconciliation, workflow coordination, workflow adapters, and an event-driven observation plane. The proposed architecture provides reconciliation as a work model that can maintain the convergence of the desired and observed states of the network in real-time and support idempotent processes and recovery of failure. Mechanisms of consistency and clear ownership boundaries are established to minimize conflicting updates, duplication of actions, and propagation of the stale state. The automation workloads are further kept in check by back-pressure mechanisms with high request volumes and dependency failures. The framework is analyzed conceptually in the workload scaling, dependency malfunctioning, lifeless conditions and in executing scenarios, to investigate its resilience and functioning traits. The analysis shows that network-specific safety controls, the history of workflow permanence, and the reconciliation of workflow state are needed as first-class architectural elements in order to accomplish effective cloud-native network automation