LUDB++: Enabling LUDB for the Analysis of Shaped Feedforward FIFO Networks using Network Calculus
For researchers and engineers analyzing latency in Time-Sensitive Networking (TSN), this provides a more accurate method for computing delay bounds in shaped FIFO networks.
This paper extends the LUDB methodology to incorporate shaping assumptions, enabling more accurate latency bounds for feedforward FIFO networks. On 130 configurations, LUDB++ improves over the state-of-the-art ELP method by up to 9.13%.
This paper discusses how latency guarantees for non-cyclic (feedforward) First-In-First-Out (FIFO) networks with shapers can be computed within the Network Calculus (NC) framework. Shapers are methods implemented in software or hardware and may reside inside the network and at the endpoint which constrain the rate and maximum packet sizes for the transmission of specific data streams (flows) or groups thereof. Shaping can improve latencies and is an important aspect of Time-Sensitive Networking (TSN). Several methods in NC exist to analyze FIFO networks. Among them is the Least Upper Delay Bound (LUDB) methodology. So far, LUDB does not incorporate shaping assumptions into its analysis. This paper addresses this gap resulting in the new methodology called LUDB++. The evaluation on a set of different line topologies and a tree topology with a total of 130 configurations shows that LUDB++ delivers more accurate latency bounds compared to LUDB. Moreover, the Exponential Linear Program (ELP) method, which considers FIFO and shaping inside the network, yields the most accurate bounds to this date. ELP is superseded by LUDB++ for most of cases by a margin of up to 9.13%.