Scalable High Speed IP Scalable High Speed IP Routing LookupsRouting Lookups

Authors: M. Waldvogel, G. Varghese, J. Turner, B. Plattner

Presenter: Zhqi Qiu

Outline• Routing and the BMP (Best

Matching Prefix) problem• Basic scheme: binary search of

hash tables• Refinements: asymmetric trees,

mutated binary search and ropes• Implementation: building the tree• Performance and comparison study

Introduction

• Increases in Internet traffic demands: link speed, router data throughput, packet forward rate

• Packet forwarding rate becomes bottleneck

• Address lookup: main step in packet forwarding

• Need efficient Best Prefix Match algorithm

More about BMP

• Classless Inter-Domain Routing (CIDR) - cope with routing table growth

• Aggregation results in address prefix list in routing tables

• Packets forwarded according to Best Matching Prefix

• Goal: use hashing to find BMP

Outline• Routing and the BMP (Best

Matching Prefix) problem• Basic scheme: binary search of

hash tables• Refinements: asymmetric trees and

ropes• Implementation: building the tree• Performance and comparison study

Basic Scheme

• Hash table organized by prefix length

• Markers needed

Marker Management

• Reducing marker storage: markers only needed in levels visited by binary search when looking for P

• E. g. : In table of 8 levels:a prefix with length of 7 [111] needs markers in level 4 and 6a prefix with length of 3 [11] needs markers in level 2

False Markers

• What if table entries are: P1=1, P2=00, P3=110 in the previous example?

• Solution:to avoid backtracking, record current best matching prefix of marker M in M.bmp

Basic AlgorithmInitialize range R = array LInitialize BMP to be null stringWhile R not empty I = middle level in range R D’ = D(L[i].length) M = Search(D’, L[i].hash) If M is nil Then set R = upper R Elseif M is a prefix & not a marker Then BMP = M.bmp; break Else { BMP = M.bmp; R = lower R} Endif Endwhile

Outline• Routing and the BMP (Best

Matching Prefix) problem• Basic scheme: binary search of

hash tables• Refinements: asymmetric trees and

ropes• Implementation: building the tree• Performance and comparison study

Algorithm Refinements• To optimize, need

to exploit deeper structure in routing table

• Simplest improvement:Search only non-empty tries

Asymmetric Binary Search• Try to search the

most promising trie first

• Example: Maximize table entries while keeping tree balance

Mutating Binary Search

• The number of distinct prefix lengths rarely exceeds 12

• Every match with some marker X means that we need only search among the set of prefixes for which X is a prefix

Potential Disadvantages

• Pre-computing optimal trees increases insertion time

• Storage of binary trees for each prefix enormous

• Storage problem solved with “rope” data structure

• Rope maximum length: log2W pointers

Rope: Encoded Trees

• Only need to store the sequence of levels which binary search on a given subtrie will follow on repeated failures to find a match

• e.g.: Rope 1 contains 16, 8, 4, 2, 1• Rope algorithm:

a prefix not a marker: end of ropea prefix and a marker: X.bmp = X

Rope Search

Initialize Rope R = default search sequenceInitialize BMP = null stringWhile R not empty

i = first pointer of R D’ = D(L[i].length) M = Search(D’, L[i].hash)

If M is success Then BMP = M.bmp; R = M.rope

Endif Endwhile

Building Rope Search

• Pass 1: builds a conventional trie• Pass 2: all prefixes inserted into

hash tables, starting with shortest prefix

• Batch insertions and deletions into the table as these operations are expensive.

Outline• Routing and the BMP (Best

Matching Prefix) problem• Basic scheme: binary search of

hash tables• Refinements: asymmetric trees and

ropes• Implementation: building the tree• Performance and comparison study

Existing Approaches

• Modification of exact matching:log22N

• Trie based: BDS radix trie O(W2)• Hardware: parallelism, CAM expensive• Protocol based: IP/tag switching, still

need some IP lookups• Caching: CAM (Content Addressable

Memory)

Complexity Comparison

Performance Comparison

Conclusion

• Intellectual contribution: markers and pre-computation ensure log time in worst-case

• Solution scalable and can be implemented in software efficiently

• Future work: choice of balancing function and faster insertion algorithms