1. Chisel: A Storage-efficient, Collision-free Hash-based Network Processing Architecture Author: Jahangir Hasan, Srihari Cadambi, Venkatta Jakkula Srimat Chakradhar Publisher: ISCA 2006 Presenter: Yuen-Shuo Li Date: 2012/10/03 1

2. Outline  Introduction  Bloomier Filter  Issue – False Positive  Issue - Wildcard  Issue - Update  Performance 2

3. Introduction(1/2) There are three major families of techniques for performing LPM  TCAM  Large cost and power dissipation  Trie-Based  Large memory requirements and long lookup latencies  Hash-Based  lower power, small memory requirement, and short lookup latencies  can’t handle wildcard and have collision problem 3

4. Introduction(2/2)  Chisel(Collision-free Hashing-Scheme for LPM)  use collision-free hashing scheme called Bloomier filter  solve two key problem  false positive  wildcard  support incremental updates 4

5. Bloomier Filter(1/5)  recall Bloom Filter  a bit vector  multiple hash function  false-positive problem 5

6. Bloomier Filter(2/5)  an extension of Bloom filter  collision-free hashing scheme  support storage and retrieval of arbitrary per-key information  support static sets of keys and not dynamic update  inherit Bloom filter’s false-positive problem 6

7. Bloomier Filter(3/5)  Idea  find a T(t) among HN(t) such that there is a one-to-one mapping between all t and T(t)  setup the Index Table, so that a lookup for t return location T(t)  and store value in a separate Result Table at address T(t) 7 HN: The set of k hash values of a key

8. Bloomier Filter(4/5) 8 It is possible that this setup process can fail use more hash function or expand the table and use a little TCAM

9. Bloomier Filter(5/5)  In order to retrieve T(t), the solution is to store some value in T(t)  use XOR 9

10. Issue – False Positive(1/2)  can occur when lookup involves some key t which was not in the set of original keys used for setup  two way  add a checksum field to each hash bucket  still has false positives  use chisel architecture  eliminating false positives 10

11. Issue – False Positive(2/2) 11

12. Issue – Wildcard(1/3)  Because hash functions cannot operate on wildcard bits  the way to support wildcard bits  large number of tables  results in considerable hardware complexity and cost  CPE  resulting in huge amounts of memory  prefix collapsing 12

13. Issue – Wildcard(2/3) 13

14. Issue – Wildcard(3/3) 14

15. Issue - Update  update for existing prefix is easy  just change the entry in the Result Table  remove: temporarily mark the prefix dirty  a large fraction of updates are actually route-flaps  maintain shadow copy of the date structures in software  first incrementally update the shadow copy  then transfer the data structure to the hardware engine 15 route-flaps: a prefix is added back after being recently removed

16. Performance(1/3) 16

17. Performance(2/3) 17

18. Performance(3/3) 18