🤖 AI Summary
Existing LUDB methods cannot analyze delay in feed-forward FIFO networks incorporating traffic shapers. This work addresses this limitation by integrating traffic shaping into the LUDB framework for the first time within Network Calculus, proposing an enhanced method termed LUDB++. The new approach supports modeling of shapers both at network endpoints and internal nodes, significantly improving the tightness of delay bounds while retaining computational efficiency. Experimental evaluation across 130 linear and tree topologies demonstrates that LUDB++ consistently yields tighter delay upper bounds than the original LUDB, and outperforms the current state-of-the-art ELP method in most scenarios, achieving a maximum improvement of 9.13%.
📝 Abstract
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 ELP is superseded by LUDB++ for most of cases by a margin of up to 9.13%.