# Improved Credit Bounds for the Credit-Based Shaper in Time-Sensitive Networking

## TL;DR

This paper derives and proves improved upper bounds on credit counters in the Credit-Based Shaper, enhancing the accuracy of latency and backlog bounds in Time-Sensitive Networking using network calculus.

## Contribution

It introduces new, formally proven credit upper bounds for CBS that improve upon existing bounds, aiding more precise latency analysis in TSN.

## Key findings

- New credit bounds are tighter than previous ones.
- Improved bounds lead to more accurate latency and backlog estimates.
- Formal proofs ensure the validity of the bounds.

## Abstract

In Time-Sensitive Networking (TSN), it is important to formally prove per flow latency and backlog bounds. To this end, recent works apply network calculus and obtain latency bounds from service curves. The latency component of such service curves is directly derived from upper bounds on the values of the credit counters used by the Credit-Based Shaper (CBS), an essential building-block of TSN. In this paper, we derive and formally prove credit upper bounds for CBS, which improve on existing bounds.