1. A Fast and Scalable IPv6 Packet Classification Author: Xiaoju Zhou, Xiaohong Huang, Qiong Sun, Wei Yang, Yan Ma Publisher: Network Infrastructure and Digital Content, 2009. IC-NIDC 2009 Presenter: Chin-Chung Pan Date: 2010/03/10

2. Outline Introduction IPv6 PC Algorithm Aimed at the FL features Related work FLIN Algorithm Basic Data Structure for a Group of Independent Sets Algorithm Description Update Complexities of the Algorithm Experimental Results 2

3. Introduction As IPv6 with a FL (flow label) in its header has been increasingly applied all over the world, well performed IPv6 PCs become crucial for ISP to support value-added services in future. Although FL was defined in RFC2460 as a 20-bit field in IPv6 header as a flow identifier, only one published PC algorithm is specifically designed based on IPv6 FL, Source IP Address (SA) and Destination IP Address (DA). 3

4. IPv6 PC Algorithm Aimed at the FL features As defined in RFC3697, FL acts as a flow identifier associated with SA and DA to replace traditional 5-field flow identifier (SA, DA, source port, destination port and protocol number). Two important characteristics of FL are defined. One is a FL value should be created randomly and the other is lifecycle of a FL is no more than 120 seconds. 4 VersionTraffic Class Flow Label Payload LengthNext HeaderHop Limit Source Address Destination Address

5. Related work IPv6 Packet Classification based on Flow Label, Source and Destination Addresses 5

6. FLIN Algorithm FL Decision Tree In FLIN, a red-black tree is constructed based on FLs from rules. FLIN based on a red-black trees will pay a small lookup cost because the tree is not perfectly balanced, but, in return, they get fast, bounded insertion and deletion operations. Using a red-black tree, FLIN can, thus, be indicated in situations where nodes come and go frequently. 6

7. FLIN Algorithm Basic Data Structure for a Group of Independent Sets Consider dimension i. Let R be the resulting set of ranges in this dimension. R is sorted according to the right end points. Let M be the set of selected non-overlapping ranges of R. Step1. If R is empty, then M is a maximum non-overlapping range set; terminate. Step2. From R select the range r with the smallest right end point. If there are more than one such ranges, randomly select one. Add r to M. Remove r from R all ranges that overlap with r. Go to step1. 7

8. FLIN Algorithm Basic Data Structure for a Group of Independent Sets 8

9. FLIN Algorithm Remark and Reordering To optimize the search performance of PC-SAM, the 1st index is -1, so we also remark it with -1. And the 3rd index is -1, it will be remarked as -3. Remarked indices will help to update a new rule. Then reorders rule indices in each bi by how big their end points are. For example, e21 is the end point of rule r21, in b3, e21>e31 and e31>e13, so we have b3->1(r13 index), 7(r21 index), 8(r31 index) will be reordered into b3-> 1, 8, 7. 9

10. FLIN Algorithm Remark and Reordering 10

11. FLIN Algorithm Update 11 1 st step is to follow the red-black tree update process. 2 nd step, we need to search both SA and DA groups. There are two main cases when a new rule is coming. Case one: There is an existing bi=bx. Case two: There is no existing start point equal to bx.

12. FLIN Algorithm Update 12

13. Complexities of the Algorithm The time require to perform red-black of FL is O(logN), and PC-SAM of (SA, DA) is O(I), N is the number of different FL values and I is the number of independent sets association with the FL so the total is O(logN+I). 13

14. Experimental Results One experimental scenario with 40000 rules as follows: There are 50 nodes (different FL values) in red-black tree and 800 rules on average for each node. In total 90 memory access are needed. This is the worst case plain implementation. Therefore the total storage in this scenario is 1600K. 14