BI-TE: Achieving GNN-based bandwidth indistinguishable traffic engineering in multi-domain SDN

The privacy protection issue of multi-domain traffic engineering has always been a hot topic. Consider that domains from different service providers may not be willing to expose the topology information within their own domains for security and privacy reasons. For example, in a cloud computing environment, different organizations may store data on the same cloud service platform. If the cloud service provider's security measures are insufficient, data leakage between different organizations may occur, and sensitive data of an organization may be accessed by other tenants, resulting in the leakage of business secrets or customer information. To solve the problems, a research team published their new research on 15 November 2025 in Frontiers of Computer Science co-published by Higher Education Press and Springer Nature.

The team adopt the concept of differential privacy and perturbed the domain information to achieve bandwidth indistinguishable TE. They first protected both the topology itself and the bandwidth information within the domain. Random perturbations were used for the topology. Then, Gaussian noise was added to the bandwidth information within the domain to achieve differential privacy protection. Unfortunately, perturbations may decrease the accuracy of the TE algorithm's resource allocation, negatively affecting performance. To mitigate this problem, they proposed BI-TE, which utilized a GNN-based bandwidth utilization prediction model to assist the controller in selecting the optimal forwarding path, thereby enhancing TE efficiency.

When a cross-domain request is sent to the root controller Root Controller (RC), RC inputs the perturbed network status information, cross-domain request, and routing information into the GNN model. If the predicted BU exceeds the threshold, this indicates that the bandwidth resources on the path may be insufficient. In this case, RC replaces the input routing path with the next shortest path, which is the second shortest path. Repeat this process until the prediction result of a certain path is lower than the threshold. Finally, transmission is performed based on the selected path.

Experimental results demonstrate that compared to abstraction-based hierarchical TE, BI-TE can reduce time overhead by nearly 24.35% while ensuring network bandwidth utilization close to 90%. Additionally, the fairness of allocation is also guaranteed.