SERENADE: A Parallel Iterative Algorithm for Crossbar Scheduling in Input-Queued Switches

Long Gong, Liang Liu, Sen Yang, Jun Xu, Yi Xie, Xinbing Wang
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引用次数: 1

Abstract

Most of today’s high-speed switches and routers adopt an input-queued crossbar switch architecture. Such a switch needs to compute a matching (crossbar schedule) between the input ports and output ports during each switching cycle (time slot). A key research challenge in designing large (in number of input/output ports N) input-queued crossbar switches is to develop crossbar scheduling algorithms that can compute “high quality” matchings – i.e., those that result in high switch throughput (ideally 100%) and low queueing delays for packets – at line rates. SERENA is one such algorithm: it outputs excellent matching decisions that result in 100% switch throughput and reasonably good queueing delays. However, since SERENA is a centralized algorithm with O(N) time complexity, it cannot support switches that both are large and have a very high line rate per port. In this work, we propose SERENADE (SERENA, the Distributed Edition), a parallel iterative algorithm that provably precisely emulates SERENA in only O(logN) iterations between input ports and output ports, and hence has a time complexity of only O(logN) per port.
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基于并行迭代算法的输入排队交换机交叉排程
当今大多数高速交换机和路由器都采用输入排队交叉排交换机架构。这种交换需要在每个交换周期(时隙)中计算输入端口和输出端口之间的匹配(交叉排程)。在设计大型(输入/输出端口数量N)输入排队的交叉排交换机时,一个关键的研究挑战是开发能够计算“高质量”匹配的交叉排调度算法——即,那些导致高交换机吞吐量(理想情况下是100%)和低分组排队延迟的算法——以线速率。SERENA就是这样一种算法:它输出优秀的匹配决策,导致100%的交换机吞吐量和相当好的排队延迟。但是,由于SERENA是一个时间复杂度为0 (N)的集中式算法,因此它不能支持既大又具有每个端口非常高的线路速率的交换机。在这项工作中,我们提出了SERENADE (SERENA,分布式版本),这是一种并行迭代算法,可以证明它在输入端口和输出端口之间仅O(logN)次迭代中精确地模拟SERENA,因此每个端口的时间复杂度仅为O(logN)。
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Impact of Large-Scale Correlated Failures on Multilevel Virtualized Networks HPSR 2020 Table of Contents SERENADE: A Parallel Iterative Algorithm for Crossbar Scheduling in Input-Queued Switches Qualitative Communication Via Network Coding and New IP : Invited Paper i-CEON: Information-centric Elastic Optical Transport Network for Efficient Content Delivery
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