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arXiv 2402.09115cs.NI
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可重构数据中心网络的集成拓扑与流量工程

Integrated Topology and Traffic Engineering for Reconfigurable Datacenter Networks

Chen Griner, Chen Avin

AI总结:

本文针对可重构数据中心网络,提出统一模型研究动态拓扑与流量工程的吞吐量,设计结合需求无关与需求感知调度的系统,在多种流量下性能提升达25%。

摘要:

最先进的数据中心网络拓扑是固定的,基于电交换技术,到目前为止,我们对其吞吐量和成本已有充分了解。在过去的几年中,研究人员一直在开发新型光交换技术,使得支持动态物理拓扑的可重构数据中心网络(RDCNs)得以出现。动态拓扑的网络设计艺术,即“拓扑工程”,仍处于起步阶段。不同的设计提供了不同的优势,例如更快的交换机重配置时间或需求感知拓扑,并且到目前为止,尚不清楚哪种设计能使吞吐量最大化。本文旨在提高我们的分析理解,通过提出一个用于动态网络及其拓扑和流量工程的通用统一模型,正式研究可重构网络的吞吐量。我们使用我们的模型来研究需求无关和需求感知系统,并证明系统吞吐量的新上界作为其拓扑和流量调度的函数。接下来,我们提供一种结合了需求无关和需求感知调度的新颖系统设计,并证明其在大量需求矩阵族下的吞吐量优势。我们在数值上针对稀疏和密集流量评估了我们的设计,并表明使用常见的网络参数,我们的方法可以比其他设计高出多达25%的性能。

英文摘要:

The state-of-the-art topologies of datacenter networks are fixed, based on electrical switching technology, and by now, we understand their throughput and cost well. For the past years, researchers have been developing novel optical switching technologies that enable the emergence of reconfigurable datacenter networks (RDCNs) that support dynamic psychical topologies. The art of network design of dynamic topologies, i.e., 'Topology Engineering,' is still in its infancy. Different designs offer distinct advantages, such as faster switch reconfiguration times or demand-aware topologies, and to date, it is yet unclear what design maximizes the throughput. This paper aims to improve our analytical understanding and formally studies the throughput of reconfigurable networks by presenting a general and unifying model for dynamic networks and their topology and traffic engineering. We use our model to study demand-oblivious and demand-aware systems and prove new upper bounds for the throughput of a system as a function of its topology and traffic schedules. Next, we offer a novel system design that combines both demand-oblivious and demand-aware schedules, and we prove its throughput supremacy under a large family of demand matrices. We evaluate our design numerically for sparse and dense traffic and show that our approach can outperform other designs by up to 25% using common network parameters.

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。

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