Generalizing Network Calculus Analysis to Derive Performance Guarantees for Multicast Flows
Generalizing Network Calculus Analysis to Derive Performance Guarantees for Multicast Flows
复制标题
推广网络演算分析以得出组播流的性能保证
DOI：
10.4108/eai.25-10-2016.2266598
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Fabien Geyer
中科院分区：
文献类型：
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作者：
Steffen Bondorf;Fabien Geyer
保证数据流的性能界限是网络工程和具有实时约束的网络认证的重要组成部分。一种普遍的分析方法是确定网络演算(DNC)，以获得端到端延迟和BU↵ER大小的保证。由于DNC系统模型，一个决定性的限制是只能分析单播流。以前对具有组播流的网络进行分析的尝试绕过了这一限制，而不是克服了它。例如，他们用过于悲观的系统模型替换了系统模型，该模型仅由单播流组成。这种方法会降低建模精度，因此不可避免地会导致不准确的性能界限。本文对组播流的↵问题进行了深入的研究。我们从现有的DNC分析开始，并将其推广到处理组播流。我们提出了一种新颖的分析程序，该程序使网络模型保持不变，保持其准确性，允许DNC原则，如只需一次付费多路复用，因此比现有方法得出更准确的性能界限。
Guaranteeing performance bounds of data flows is an essential part of network engineering and certification of networks with real-time constraints. A prevalent analytical method to derive guarantees for end-to-end delay and bu↵er size is Deterministic Network Calculus (DNC). Due to the DNC system model, one decisive restriction is that only unicast flows can be analyzed. Previous attempts to analyze networks with multicast flows circumvented this restriction instead of overcoming it. E.g., they replaced the system model with an overly-pessimistic one that consists of unicast flows only. Such approaches impair modeling accuracy and thus inevitably result in inaccurate performance bounds. In this paper, we approach the problem of multicast flows di↵erently. We start from existing DNC analyses and generalize them to handle multicast flows. We contribute a novel analysis procedure that leaves the network model unaltered, preserves its accuracy, allows for DNC principles such as pay multiplexing only once, and therefore derives more accurate performance bounds than existing approaches.