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Fast Updating Algorithms for TCAMs Devavrat Shah Pankaj Gupta IEEE MICRO, Jan.-Feb. 2001.

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Presentation on theme: "Fast Updating Algorithms for TCAMs Devavrat Shah Pankaj Gupta IEEE MICRO, Jan.-Feb. 2001."— Presentation transcript:

1 Fast Updating Algorithms for TCAMs Devavrat Shah Pankaj Gupta IEEE MICRO, Jan.-Feb. 2001

2 Outline Background Introduction to TCAMs Provided algorithms Simulation Conclusion and discussion

3 Background A collection of rules is called a policy database or a classifier Packet classification To find a best matching rule Identification of the flow Generalization of the routing lookup Routers must keep pace with link speeds

4 Intro. to TCAMs Fully associative memory Parallel search for every element in the array Take three logic states 0, 1, or don ’ t-care X Store each field as a (var, mask) pair (10000, 11000) represents the 5-bit prefix 10* Store forwarding table entries in order of decreasing prefix lengths (priorities) The lower address has higher priority

5 Intro. to TCAMs (cont ’ d) Increasingly being deployed Simplicity and speed (10 ns per clock) Forwarding table updates complicate keeping the list of prefixes in sorted order

6 Intro. to TCAMs (cont ’ d)

7 If a new prefix /18 is added to the table There ’ s no empty space between prefixes P1 and P2! There are two intuitive solutions below.

8 Intuitive solution 1 Shift prefixes P2 to P5 downward by one location each This costs O(N), where N is the # of prefixes

9 Intuitive solution 2 Keeps a few empty memory locations at all X nonempty ones Degenerates to O(N) if empty space is filled up Wastes precious CAM space

10 Provided algorithm 1 Two prefixes of the same length don ’ t need to be ordered Prefix-length ordering constraint Referred to here as L-algorithm, which can be optimized to L/2

11 Provided algorithm 1 (cont ’ d) Keep all the unused entries in the center Swap at most L/2 entries to obtain an unused entry The arrangement of Fig. 4 can again improve the time

12 Provided algorithm 2 PLO constraint is more restrictive The constraint can be relaxed to only overlapping prefixes Prefixes that lie on the same chain of the trie need to be ordered Chain-ancestor ordering constraint

13 Provided algorithm 2 (cont ’ d) There ’ s an ordering constraint between pi and pj iff one is a prefix of the other ex /24 and /16 Decrease the update to D/2, where D is the max length of any chain in the trie

14 Provided algorithm 2 (cont ’ d) Terminology LC(p) len(LC(p)) rootpath(p) ancestor of p prefix-child of p hcld(p) HCN(p)

15 Insertion Case I. Assume q is to be inserted above the pool One empty entry can be created by moving prefixes on LC(q) downward one by one, starting from p j to the border of the pool The movement is less than D/2, where D is len(LC(q))

16 Insertion (cont ’ d) Case II. Assume q is to be inserted below the pool One empty entry can be created by moving prefixes on HCN(q) upward one by one, starting from p j to the border of the pool Again, the movement is less than D/2 pjpj pipi P i + 1

17 Deletion Two exceptions compared with insertion 1. It works in reverse 2. It works on the chain that has prefix p adjacent to the free space pool The new unused entry is rippled by moving prefixes downward on the chain

18 Auxiliary trie data structure To determine LC(p) and HCN(p) quickly, the following additional fields in every trie node are maintained wt(p): the weight of each node p wt_ptr(p): the child with the highest weight hcld_ptr(p): the child at the highest location The weight of each node

19 Simulation

20 Simulation (cont ’ d)

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24 Conclusion and discussion The PLO_OPT algorithm improves update speed by a factor of two over the best- known solution The CAO_OPT algorithm completes one prefix update in slightly greater than one memory movement per update operation Some disadvantages of TCAMs More prices, more power consumption, less capacity, dealing with prefixes only


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