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Conflict detection and resolution in two-dimensional prefix router tables

机译:二维前缀路由器表中的冲突检测和解决

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We show that determining the minimum number of resolve filters that need to be added to a set of two-dimensional (2-D) prefix filters so that the filter set can implement a given policy using the first-matching-rule-in-table tie breaker is NP-hard. Additionally, we develop a fast O(nlogn+s) time, where n is the number of filters and s is the number of conflicts, plane-sweep algorithm to detect and report all pairs of conflicting 2-D prefix filters. The space complexity of our algorithm is O(n). On our test set of 15 2-D filter sets, our algorithm runs between 4 and 17 times as fast as the 2-D trie algorithm of A. Hari et al. (2000) and uses between 1/4th and 1/8th the memory used by the algorithm of Hari et al. On the same test set, our algorithm is between 4 and 27 times as fast as the bit-vector algorithm of Baboescu and Varghese (2002) and uses between 1/205 and 1/6 as much memory. We introduce the notion of an essential resolve filter and develop an efficient algorithm to determine the essential resolve filters of a prefix filter set.
机译:我们展示了确定需要添加到一组二维(2-D)前缀过滤器中的最小数量的解析过滤器,以便过滤器集可以使用表中的first-matching-rule-in-table实施给定策​​略决胜局是NP-hard。此外,我们开发了快速的O(nlogn + s)时间,其中n是过滤器数,s是冲突数,平面扫描算法可检测并报告所有成对的二维前缀过滤器。我们算法的空间复杂度为O(n)。在我们的15个2-D滤波器集的测试集上,我们的算法的运行速度是A. Hari等人的2-D trie算法的4到17倍。 (2000年),并使用Hari等人的算法所用内存的1/4至1/8。在同一测试集上,我们的算法的速度是Baboescu和Varghese(2002)的位向量算法的4到27倍,并使用了1/205到1/6的内存。我们介绍了基本解析过滤器的概念,并开发了一种有效的算法来确定前缀过滤器集的基本解析过滤器。

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