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Demystifying Deep Learning in Networking

We are witnessing a surge of efforts in networking community to develop deep neural networks (DNNs) based approaches to networking problems. Most results so far have been remarkably promising, which is arguably surprising given how intensively these problems have been studied before. Despite these promises, there has not been much systematic work to understand the inner workings of these DNNs trained in networking settings, their generalizability in different workloads, and their potential synergy with domain-specific knowledge. The problem of model opacity would eventually impede the adoption of DNN-based solutions in practice. This position paper marks the first attempt to shed light on the interpretability of DNNs used in networking problems. Inspired by recent research in ML towards interpretable ML models, we call upon this community to similarly develop techniques and leverage domain-specific insights to demystify the DNNs trained in networking settings, and ultimately unleash the potential of DNNs in an explainable and reliable way.