队列比例抽样:一种更好的输入排队交换机交叉排程方法

Long Gong, Paul Tune, Liang Liu, Sen Yang, Jun Xu
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引用次数: 8

摘要

目前大多数交换系统,如因特网路由器和数据中心交换机,都使用一个输入排队的交叉排来连接输入端口和输出端口。这样的开关需要为每个开关周期(时隙)计算输入和输出端口之间的匹配。设计这种匹配算法的主要挑战是处理计算匹配质量和算法计算复杂度之间的不幸权衡。在本文中,我们提出了一种通用方法,可以显着提高SERENA和iSLIP的性能,但在每个输入/输出端口仅产生O(1)额外的计算复杂度。我们的方法是一种新颖的提议策略,称为队列比例抽样(QPS),它产生了一个优秀的启动器匹配。我们通过严格的模拟表明,当从这个启动器匹配开始时,iSLIP和SERENA可以输出更好的最终匹配决策,通过由此产生的吞吐量和延迟性能来衡量,而不是其他方式。
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Queue-Proportional Sampling: A Better Approach to Crossbar Scheduling for Input-Queued Switches
Most present day switching systems, in Internet routers and data-center switches, employ a single input-queued crossbar to interconnect input ports with output ports. Such switches need to compute a matching, between input and output ports, for each switching cycle (time slot). The main challenge in designing such matching algorithms is to deal with the unfortunate tradeoff between the quality of the computed matching and the computational complexity of the algorithm. In this paper, we propose a general approach that can significantly boost the performance of both SERENA and iSLIP, yet incurs only O(1) additional computational complexity at each input/output port. Our approach is a novel proposing strategy, called Queue-Proportional Sampling (QPS), that generates an excellent starter matching. We show, through rigorous simulations, that when starting with this starter matching, iSLIP and SERENA can output much better final matching decisions, as measured by the resulting throughput and delay performance, than they otherwise can.
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