A Memory-Efficient Hashing by Multi-Predicate Bloom Filters for Packet Classification

Abstract

Hash tables (HTs) are poorly designed for multiple off-chip memory accesses during packet classification and critically affect throughput in high-speed routers. Therefore, an HT with fast on-chip memory and high-capacity off-chip memory for predictable lookup-throughput is desirable. Both a legacy HT (LHT) and a recently proposed fast HT (FHT) [1] have the disadvantage of memory overhead due to pointers and duplicate items in linked lists. Also, memory usage for an FHT did not consider the bits in counters for fair comparision with an LHT. In this paper, we propose a novel hash architecture called a Multi-predicate Bloom-filtered HT (MBHT) using parallel Bloom filters and generating off-chip memory addresses in the base-2 x number system, x∈{1, 2, · · · }, which removes the overhead of pointers. Using a larger base of number system, an MBHT reduces on-chip memory size by a factor of log 2 b 2 / log 2 b 1 where b 1 and b 2 are bases of number system (b 2 >b 1 ). Compared to an FHT, the MBHT is approximately x(log 2 n + 4)/(2 log 2 n) times more efficient for on-chip memory, where n is the number of keys. This results in a significant reduction in the number of offchip memory accesses. A simulation with a dataset of packets from NLANR [2] shows the on-chip memory reductions by 1.7 and 2 times over an LHT and an FHT are made. Besides, an MBHT of base-16 needs less off-chip memory accesses by 2117 in total URL queries of NLANR, compared to an FHT.This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE INFOCOM 2008 proceedings.

A Bloom Filter with the Integrated Hash Table Using an Additional Hashing Function

“…It only works in conjunction with counting Bloom filters and needs to reconsider all of the already inserted items for each item that consequently leads to longer processing time. In [10], a hash architecture called a Multi-predicate Bloom-filtered Hash Table (MBHT) using parallel Bloom filters is presented. It is generated off-chip memory addresses in the base-2 x number system, x ∈ {1, 2, ...}, which removes the overhead of pointers.…”

Bloom Filter Query Algorithm

“…• 网络测量方法 [16−23] :利用布鲁姆过滤器为网络业务流计数,当流量超过阈值时产生告警并进行拥塞控 制 [16,17] ;在进行 IP 数据包回溯时,通过在路由器中用布鲁姆过滤器将已经转发过的数据报文记录下来 [18,19] ,只要 查找该数据包是否属于转发记录中的数据包即可完成数据包的回溯检查;布鲁姆过滤器还广泛应用于网络测 量中,包括:网络数据包分类 [20,24] 、网络抽样 [21] 、抽样的还原 [22] 以及流分布估计 [23] 等.…”

Elastic Bloom Filter: Deletable and Expandable Filter Using Elastic Fingerprints

“…The main advantages of Bloom filters are: (i) small memory footprint, (ii) fast and constant speed of queries and updates, (iii) no false negatives, small and tunable false positive rate. Due to these advantages, the Bloom filter and its variants have been widely used in a great many fields, such as real-time systems [24], computer architectures [21], neural network [17], IP lookups [10], [18], [23], web caching [13], Internet measurement [11], packet classification [38], regular expression matching [9] , multicast [32], queue management [8], routing [31], [35], P2P networks [20], [30], data center networks [39], cloud computing [26], and more [16], [28], [37].…”