Flow-Aware, Machine Learning-Driven Mapping for Serverless Software Defined Networks

Energy efficiency has become a critical challenge in Software-Defined Networking (SDN), particularly as modern controllers increasingly rely on computationally intensive software components. In parallel, server-less computing has appeared as a lightweight, event-driven execution model that offers fine-grained resource utilisation and reduced operational overhead. While recent studies have explored integrating serverless platforms into SDN architectures, they largely focus on architectural disaggregation and overlook energy-aware resource provisioning decisions. This paper proposes a Machine Learning (ML)-driven, flow-aware architecture for energy-aware resource provisioning in serverless SDN environments. The proposed approach predicts per-flow energy consumption, processing time, and CPU usage using machine learning models trained on flow-level features. These predictions are used to dynamically map incoming data flows to the most energy-efficient execution nodes. To support accurat e prediction, we introduce a methodology for fine-grained measurement of per-flow energy consumption and processing time in serverless SDN environments. The architecture is evaluated using an SDN–serverless testbed built with ONOS and Knative, leveraging real SDN traffic traces. Experimental results demonstrate that the proposed solution effectively reduces energy consumption compared to baseline deployments while maintaining service quality, highlighting the benefits of flow-aware, ML-based resource provisioning in serverless SDN systems.