FlowDyn:使用可编程数据平面实现动态流间隙检测

C. H. Benet, A. Kassler
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引用次数: 2

摘要

数据中心网络在机架顶交换机(ToR)之间提供多条不相交路径,以连接服务器机架,从而提供大的对分带宽。为了充分利用多路径的可用容量,需要有效的负载平衡机制。虽然基于包的负载平衡可以实现高利用率,但它会受到重排序的影响。基于流的负载平衡(如等价多路径路由(ECMP))将流量均匀地分布在多个路径上,导致频繁的哈希冲突和次优性能。最后,基于流的负载平衡(如CONGA或HULA)将流分成更小的单元,这些单元在不同的路径上发送。大多数基于流的负载平衡方案依赖于流间隙的适当静态设置,该设置决定何时检测到新流。虽然过小的差距可能导致重新排序,但过大的差距会导致错过负载平衡的机会。在本文中,我们提出了FlowDyn,它动态地适应流间隙以提高负载平衡方案的效率,同时避免了重排序问题。使用可编程数据平面,FlowDyn使用主动探针和遥测信息来跟踪不同ToR交换机之间的路径延迟。FlowDyn动态计算合适的流间隙,可用于基于流的负载平衡机制。我们在模拟中对FlowDyn进行了广泛的评估,表明它在10%负载下的流量完成时间缩短了3.19倍,在90%负载下的流量完成时间缩短了1.16倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
FlowDyn: Towards a Dynamic Flowlet Gap Detection using Programmable Data Planes
Data center networks offer multiple disjoint paths between Top-of-Rack (ToR) switches to connect server racks providing large bisection bandwidth. An effective load-balancing mechanism is required in order to fully utilize the available capacity of the multiple paths. While packet-based load-balancing can achieve high utilization, it suffers from reordering. Flow-based load-balancing such as equal-cost multipath routing (ECMP) spreads traffic uniformly across multiple paths leading to frequent hash collisions and suboptimal performance. Finally, flowlet based load-balancing such as CONGA or HULA splits flows into smaller units, which are sent on different paths. Most flowlet based load-balancing schemes depend on a proper static setting of the flowlet gap, which decides when new flowlets are detected. While a too small gap may lead to reordering, a too large gap results in missed load-balancing opportunities. In this paper, we propose FlowDyn, which dynamically adapts the flowlet gap to increase the efficiency of the load-balancing schemes while avoiding the reordering problem. Using programmable data planes, FlowDyn uses active probes together with telemetry information to track path latency between different ToR switches. FlowDyn calculates dynamically a suitable flowlet gap that can be used for flowlet based load-balancing mechanism. We evaluate FlowDyn extensively in simulation, showing that it achieves 3.19 times smaller flow completion time at 10% load and 1.16x at 90% load.
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