时间敏感网络的网络演算边界：重新审视
Network Calculus Bounds for Time-Sensitive Networks: A Revisit
AI总结：
本文重新审视时间敏感网络的网络演算边界，指出基础 min-plus 服务模型常忽视分组化效应，为此引入结合 min-plus 与 max-plus 分支的集成分析方法，扩展出 $g^{x}$-server 模型以推导出改进的服务与延迟边界。
摘要：
网络演算（NC），特别是其 min-plus 分支，已被广泛用于为时间敏感网络（TSN）构建服务模型并计算延迟边界。本文重新审视了这些基本结果。特别地，针对为 TSN 提出并用于计算延迟边界的最基本 min-plus 服务模型，其反例表明分组化效应常被忽视。为解决此问题，本文还考虑了 NC 的 max-plus 分支，其模型能更明确地处理分组化流量。研究发现，将 min-plus 模型映射到 max-plus 模型可能会对由 min-plus 分析得出的延迟边界带来直接改进。此外，引入了一种结合 min-plus 和 max-plus NC 分支模型的集成分析方法。在此方法中，扩展了 max-plus $g$-server 模型，该扩展模型称为 $g^{x}$-server，并与 min-plus 到达曲线流量模型结合使用。通过应用该集成 NC 方法，推导出了 TSN 中几个基本设置的服务和延迟边界。
英文摘要：
Network calculus (NC), particularly its min-plus branch, has been extensively utilized to construct service models and compute delay bounds for time-sensitive networks (TSNs). This paper provides a revisit to the fundamental results. In particular, counterexamples to the most basic min-plus service models, which have been proposed for TSNs and used for computing delay bounds, indicate that the packetization effect has often been overlooked. To address, the max-plus branch of NC is also considered in this paper, whose models handle packetized traffic more explicitly. It is found that mapping the min-plus models to the max-plus models may bring in an immediate improvement over delay bounds derived from the min-plus analysis. In addition, an integrated analytical approach that combines models from both the min-plus and the max-plus NC branches is introduced. In this approach, the max-plus $g$-server model is extended and the extended model, called $g^{x}$-server, is used together with the min-plus arrival curve traffic model. By applying the integrated NC approach, service and delay bounds are derived for several settings that are fundamental in TSNs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
