  Modern industrial networks transport both best-effort and real-time traffic.
Time-Sensitive Networking (TSN) was introduced by the IEEE TSN Task Group as an
enhancement to Ethernet to provide high quality of service (QoS) for real-time
traffic. In a TSN network, applications signal their QoS requirements to the
network before transmitting data. The network then allocates resources to meet
these requirements. However, TSN-unaware applications can neither perform this
registration process nor profit from TSN's QoS benefits. The contributions of
this paper are twofold. First, we introduce a novel network architecture in
which an additional device autonomously signals the QoS requirements of
TSN-unaware applications to the network. Second, we propose a processing method
to detect real-time streams in a network and extract the necessary information
for the TSN stream signaling. It leverages a Deep Recurrent Neural Network
(DRNN) to detect periodic traffic, extracts an accurate traffic description,
and uses traffic classification to determine the source application. As a
result, our proposal allows TSN-unaware applications to benefit from TSNs QoS
guarantees. Our evaluations underline the effectiveness of the proposed
architecture and processing method.
