Handling Traffic Uncertainty in Failure-Tolerant Traffic Engineering

Network failure uncertainty and traffic demand uncertainty pose a series of challenges to efficiently solve network traffic engineering (TE) problems in the production wide-area networks (WANs). Prior works especially predict-then-optimize paradigm in TE systems encounter a significant mismatch between the upstream prediction task and the downstream optimization task, leading to potential demand loss risk and further degrading the performance of customer-facing applications in the cloud. In this paper, we present TUFTTE, a risk-driven TE optimization framework, where the objective function and constraints can be flexibly specified by the network operators. We employ a differentiable optimization layer to guide the prediction model with the objective of minimizing the network risks. When the network risk is defined as the demand loss, our approach can provably converge to the global optimum. Experimental results on real-world topology demonstrate that TUFTTE reduces the demand loss by 11.59% on average compared to the state-of-the- art algorithm. We believe that our paper deeply explores the potential of prediction and promotes the understanding on how to develop and deploy a general learning module into the commercial TE optimization.