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Application of Bloom filters in Fast Packet Classification

机译:Bloom过滤器在快速分组分类中的应用

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摘要

Packet classification finds various applications in computer networks like QoS, Firewalls, multimedia communication, telecommunication, security, monitoring data traffic etc. To classify packets to a particular flow or the set of flows, Intermediate nodes which are present in the network must perform search for a rule which defines the flow of that particular packet which is chosen on the basis of different field present in the data packet. The rule set is predefined by the user, which is constructed on the basis of algorithmic and architectural methodology. The major constraint in this methodology is searching speed of particular rule. Since few decades researches are finding the best computational methodology for packet classification. But the current algorithms which are used in the packet classification highly rely on expensive and high power consuming devices like TCAM (Ternary content addressable memory). Therefore searching of fast and power efficient algorithms for packet classification is still the subject of interest for researchers. In this paper we have delivered a new direction to packet classification which includes algorithmic and architectural structure for packet classification. Our inception is from well-known Cross product algorithm which is very fast but introduce additional rules which increases memory requirement. We have shown how to enhance the crossproduct in a way which drastically reduces this addition of extra rules, without affecting the throughput of the algorithm, unnecessary memory access to the off chip memory are avoided by filtering them through on chip bloom filter. For packets that matches p rules in a rule set, our algorithm requires only P+4+ε memory access to find all the matching rules. Where ε «1 which is a constant that depends on small false positive rate of Bloom filter. Using two SRAM chips search speed of 38 million packets per second can be achieved. For the rule set size of few hundred to thousand rules an average rules set expansion factor is 1.2 to 1.4 and average memory consumption is 32 to 45 bytes.
机译:数据包分类可在计算机网络中找到各种应用,例如QoS,防火墙,多媒体通信,电信,安全性,监视数据流量等。要将数据包分类为特定流或流集,网络中存在的中间节点必须执行搜索定义该特定分组流的规则,该规则是根据数据分组中存在的不同字段选择的。规则集由用户预定义,它是基于算法和体系结构方法构建的。这种方法的主要限制是搜索特定规则的速度。几十年来,研究一直在寻找用于分组分类的最佳计算方法。但是,当前在分组分类中使用的算法高度依赖于昂贵且高功耗的设备,例如TCAM(三进制内容可寻址存储器)。因此,对于分组分类的快速高效功率算法的搜索仍然是研究人员感兴趣的主题。在本文中,我们为数据包分类提供了一个新的方向,其中包括用于数据包分类的算法和体系结构。我们的开始是基于著名的Cross product算法,该算法非常快,但是引入了附加规则,从而增加了内存需求。我们已经展示了如何以大幅减少这种额外规则添加的方式来提高叉积,而又不影响算法的吞吐量,通过片上bloom过滤器对其进行过滤可以避免对片外存储器的不必要的存储器访问。对于与规则集中的p条规则匹配的数据包,我们的算法仅需要P + 4 +ε内存访问即可找到所有匹配的规则。其中ε«1是一个常数,该常数取决于较小的Bloom Bloom假阳性率。使用两个SRAM芯片,可以实现每秒3800万个数据包的搜索速度。对于几百到几千条规则的规则集大小,平均规则集扩展因子为1.2到1.4,平均内存消耗为32到45字节。

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