Scalable data center multicast using multi-class Bloom Filter
Scalable data center multicast using multi-class Bloom Filter
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DOI：
10.1109/icnp.2011.6089061
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
Dan Li;Henggang Cui;Yan Hu;Yong Xia;Xin Wang
中科院分区：
文献类型：
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作者：
Dan Li;Henggang Cui;Yan Hu;Yong Xia;Xin Wang
组播有利于数据中心组通信节省网络带宽并提高应用程序吞吐量。然而，由于交换机中的转发表内存空间有限，尤其是现代数据中心常用的低端交换机，扩展多播以支持数以万计的并发组通信具有挑战性。布隆过滤器是压缩组播转发表的有效工具，但当组成员资格测试误报时，可能会发生严重的流量泄漏。为了减少多播流量泄漏，本文提出了一种新颖的多类布隆过滤器（MBF），它通过包含元素不确定性来扩展标准布隆过滤器。具体来说，MBF 根据每个多播组插入布隆过滤器的概率，在每个元素级别设置哈希函数的数量。我们设计了一个简单而有效的算法来计算每个多播组的哈希函数的数量。我们在 Linux 平台上制作了基于软件的 MBF 转发引擎原型。仿真和原型评估结果表明，与标准布隆过滤器相比，MBF 可以显着减少组播流量泄漏，同时几乎不会造成系统开销。
Multicast benefits data center group communications in saving network bandwidth and increasing application throughput. However, it is challenging to scale Multicast to support tens of thousands of concurrent group communications due to limited forwarding table memory space in the switches, particularly the low-end ones commonly used in modern data centers. Bloom Filter is an efficient tool to compress the Multicast forwarding table, but significant traffic leakage may occur when group membership testing is false positive. To reduce the Multicast traffic leakage, in this paper we bring forward a novel multi-class Bloom Filter (MBF), which extends the standard Bloom Filter by embracing element uncertainty. Specifically, MBF sets the number of hash functions in a per-element level, based on the probability for each Multicast group to be inserted into the Bloom Filter. We design a simple yet effective algorithm to calculate the number of hash functions for each Multicast group. We have prototyped a software based MBF forwarding engine on the Linux platform. Simulation and prototype evaluation results demonstrate that MBF can significantly reduce Multicast traffic leakage compared to the standard Bloom Filter, while causing little system overhead.