LUDB++: Enabling LUDB for the Analysis of Shaped Feedforward FIFO Networks using Network Calculus

TL;DR

This paper introduces LUDB++, a new method that enhances latency bound analysis in FIFO networks with shapers within the Network Calculus framework, providing more accurate results than previous methods.

Contribution

LUDB++ extends the LUDB methodology to incorporate shaping assumptions, improving latency bound accuracy in non-cyclic FIFO networks with shapers.

Findings

LUDB++ outperforms LUDB in accuracy across various network topologies.

The Exponential Linear Program (ELP) method provides the most accurate bounds but is superseded by LUDB++ in most cases.

LUDB++ achieves up to 9.13% better bounds than existing methods.

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 ELP is superseded by LUDB++ for most of cases by a margin of up to 9.13%.