Abstract
In recent years the Time-Sensitive Networking (TSN) Task Group (TG) has been working on proposing a series of standards to provide Ethernet with hard real-time guarantees, online management of the traffic and fault tolerance mechanisms. In this way the TG expects to create the network technology of future novel applications with real-time and reliability requirements. TSN proposes using spatial redundancy to increase the reliability of Ethernet networks, but using spatial redundancy to tolerate temporary faults is not a cost-effective solution. For this reason, we propose to use time redundancy to tolerate temporary faults in the links of TSN-based networks. Specifically, we have proposed the Proactive Transmission of Replicated Frames (PTRF) mechanism, which consists in transmitting several copies of each frame in a preventive manner. In this article we present for the first time a detailed description of the mechanism, with the three different approaches we have designed. We also present the implementation of PTRF in a real TSN prototype. Furthermore, we carry out a qualitative comparison of the different approaches of the mechanism and we experimentally evaluate the approaches of the mechanism in a quantitative manner from three perspectives: the end-to-end delay, the jitter and the bandwidth consumption.
Highlights
In the last two decades several novel industrial applications have emerged, such as Industry 4.0 systems [1], autonomous vehicles [2] or efficient energy management infrastructures [3]
In order to provide Time-Sensitive Networking (TSN) networks with adequate mechanisms to tolerate temporary faults affecting the channel, while overcoming the automatic repeat request (ARQ) limitations, we have proposed a time redundancy mechanism called Proactive Transmission of Replicated Frames (PTRF)
All the mechanisms proposed by the TSN Task Group (TG) operate at the layer 2 and PTRF is designed to operate in such layer
Summary
In the last two decades several novel industrial applications have emerged, such as Industry 4.0 systems [1], autonomous vehicles [2] or efficient energy management infrastructures [3] These applications usually have some common requirements. They normally interact with the real world This imposes tight timing constraints, so that these applications must provide their services in real time, i.e., they must produce their results within a bounded time. They frequently are considered to be critical, as their failure could have catastrophic consequences. They must be highly dependable in general and highly reliable in particular, i.e., they must provide a correct service throughout a given interval of time with a very high probability
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