Forecasting-Oriented Management of Software-Defined Fabric Environments

• Ariel L. C. Portela UECE
• Maria C. M. M. Ferreira UECE
• Rafael L. Gomes UECE

Resumo

The growing adoption of programmable network architectures, such as ONF’s SD-Fabric, has introduced new levels of flexibility, automation, and control into modern infrastructures. Leveraging P4-programmable data planes, centralized SDN control, and in-band telemetry, these architectures are well-suited to meet the stringent requirements of dynamic, cloud-native, and edge computing environments. However, ensuring service reliability, performance, and compliance with strict Service Level Agreements (SLAs) remains a complex challenge, particularly under high traffic dynamics and resource variability. Within this context, this paper presents a PhD research project that proposes a Forecasting-Oriented Management framework for SD-Fabric environments, combining in-band telemetry, AI-based forecasting, and a hierarchical distributed control strategy to anticipate and mitigate SLA violations. The solution proactively identifies network risks, such as failures, bottlenecks, and resource constraints, enabling intelligent decisions on traffic engineering and resource reallocation. Initial experiments, conducted in an emulated environment using P4-programmed switches and a simulated control layer, allowed comprehensive testing under varied traffic and failure scenarios. These preliminary results demonstrate the potential of the proposed approach to enhance SLA compliance and improve the resilience and efficiency of programmable network fabrics.