EE384Y: Packet Switch Architectures Part II Address Lookup and Classification (2) Nick McKeown Professor of Electrical Engineering and Computer Science, Stanford University nickm@stanford.edu http://www.stanford.edu/~nickm

Outline Routing Lookups Packet Classification Motivation and problem definition Classification algorithms Linear search Associative search (TCAM) Trie-based techniques Crossproducting Tradeoffs in classification Heuristic algorithms References

Motivation: Desire for Additional Services Y ISP3 X NAP ISP1 Z ISP2 Service Example Differentiated Service Ensure that traffic from ISP2 is given higher priority over traffic from ISP3. Packet Filtering Deny all web traffic from ISP3 at interface X. Policy-based routing Ensure that all web traffic from ISP2 is sent via interface Z. Other examples: Accounting & billing, rate-limiting, etc.

Special Processing Requires Identification of Flows All packets of a flow obey a pre-defined rule and are processed similarly by the router E.g. a flow = (src-IP-address, dst-IP-address), or a flow = (dst-IP-prefix, protocol) etc. Router needs to identify the flow of every incoming packet and then perform appropriate special processing based on negotiated service agreements Classification Rules or policies (aka ACL entries, filters)

Flow-aware Router: Basic Architectural Components Routing, resource reservation, admission control, SLAs Control Packet classification Special processing Switching Datapath: (per-packet processing) Routing lookup Scheduling

Multi-field Packet Classification L3-DA L3-SA L4-PROT Field 1 Field 2 … Field k Action Rule 1 5.3.40.0/21 2.13.8.11/32 UDP A1 Rule 2 5.168.3.0/24 152.133.0.0/16 TCP A2 Rule N 5.168.0.0/16 152.0.0.0/8 ANY AN Example: packet (5.168.3.32, 152.133.171.71, …, TCP) Packet Classification: Find the action associated with the highest priority rule matching an incoming packet header.

Formal Problem Definition Given a classifier C with N rules, Rj, 1  j  N, where Rj consists of three entities: A regular expression Rj[i], 1  i  d, on each of the d header fields, A number, pri(Rj), indicating the priority of the rule in the classifier, and An action, referred to as action(Rj). For an incoming packet P with the header considered as a d-tuple of points (P1, P2, …, Pd), the d-dimensional packet classification problem is to find the rule Rm with the highest priority among all the rules Rj matching the d-tuple; i.e., pri(Rm) > pri(Rj),  j  m, 1  j  N, such that Pi matches Rj[i], 1  i  d. We call rule Rm the best matching rule for packet P.

Routing Lookup: Instance of 1D Classification One-dimension (destination address) Forwarding table  classifier Routing table entry  rule Outgoing interface  action Prefix-length  priority

Example 4D Classifier R1 R2 R3 R4 R5 Rule L3-DA L3-SA L4-DP L4-PROT Action R1 152.163.190.69/255.255.255.255 152.163.80.11/255.255.255.255 * Deny R2 152.168.3/255.255.255 152.163.200.157/255.255.255.255 eq www udp R3 range 20-21 Permit R4 tcp R5

Example Classification Results Pkt Hdr L3-DA L3-SA L4-DP L4-PROT Rule, Action P1 152.163.190.69 152.163.80.11 www tcp R1, Deny P2 152.168.3.21 152.163.200.157 udp R2, Deny

Geometric Interpretation Packet classification problem: Find the highest priority rectangle containing an incoming point R7 R6 R2 R1 P1 R4 P2 R5 R3 e.g. (128.16.46.23, *) Dimension 2 e.g. (144.24/24, 64/16) Dimension 1

Outline Routing Lookups Packet Classification Motivation and problem definition Classification algorithms Linear search Associative search (TCAM) Trie-based techniques Crossproducting Tradeoffs in classification Heuristic algorithms References

Metrics for Classification Algorithms Speed Storage requirements Ability to handle large classifiers Low preprocessing time Update time Scalability in the number of header fields Flexibility in rule specification

Size/Update-rate of Classifier? Micro-flow recognition 128K-1M flows in a metro/edge router Also requires high update rate (but have few wildcards) Firewall applications <2K rules per interface Requires low update rate (usually configured at start-up/boot-up time) Depends heavily on the type of router

Linear Search Keep rules in a linked list O(N) storage, O(N) lookup time, O(1) update complexity Q. Why is update complexity O(1) ?

Ternary Match Operation Each TCAM entry stores a value, V, and mask, M Hence, two bits (Vi and Mi) for each bit position i (i=1..W) For an incoming packet header, H = {Hi}, the TCAM entry outputs a match if Hi matches Vi in each bit position for which Mi equals ‘1’. Vi Mi Match in bit position I ? X Yes 1 Iff (Hi==0) Iff (Hi==1) A simple diagram of a row entry would be useful here. Z = &_{I=1..W} (~Mi | (Vi == Hi)) Optional Exercise: What is the logic equation for Z (boolean variable denoting whether a TCAM entry matched)? Optional Exercise: What is the logic equation for Z (boolean variable denoting whether a TCAM entry matched), if instead of (Vi, Mi) we store (Ai,Bi) where (0,0) = always match, (1,1) = always mismatch, (0,1) = match0, and (1,0) = match1

Lookups/Classification with Ternary CAM TCAM RAM P32 P31 P8 For LPM Memory array Action Memory 1.23.11.3, tcp 1 1 2 3 Priority Packet Action Header encoder M 1.23.x.x, x 1

Range-to-prefix Blowup Maximum memory blowup = factor of (2W-2)d Rule Range R1 [3,11] R2 [2,7] R3 [4,11] R4 [4,7] R5 [1,14] Maximal Prefixes 0011, 01**, 10** 001*, 01** 01**, 10** 01** 0001, 001*, 01**, 10**, 110*, 1110 Luckily, real-life does not see too many arbitrary ranges.

TCAMs Advantages Disadvantages Extensible to multiple fields Fast: 10-16 ns today (66-100 M searches per second) going to 250 Msps Simple to understand and use Disadvantages Inflexible: range-to-prefix blowup Power: ~15-20W @ 100Msps Cost: $200-$250 for ~2MByte Density: largest available in 2003-4 is ~2MB, i.e., 128K x 128 (can be cascaded) Tough memory soft-error problem

Example Classifier Rule Destination Address Source Address R1 0* 10* 01* R3 1* R4 00* R5 11* R6 R7 *

Hierarchical Tries Dimension DA O(NW) memory O(W2) lookup Dimension SA Search (000,010) Rule DA SA R1 0* 10* R2 01* R3 1* R4 00* R5 11* R6 R7 * Dimension DA Dimension SA R5 R2 R1 R3 R6 R7 R4 1 O(NW) memory O(W2) lookup

Set-pruning Tries [Tsuchiya, Sri98] Search (000,010) Rule DA SA R1 0* 10* R2 01* R3 1* R4 00* R5 11* R6 R7 * Dimension DA 1 O(N2) memory O(2W) lookup R4 R3 R6 Dimension SA R7 R2 R1 R5 R7 R2 R1 R7 R7

Grid-of-Tries [Sri98] Dimension DA O(NW) memory O(2W) lookup Search (000,010) Rule DA SA R1 0* 10* R2 01* R3 1* R4 00* R5 11* R6 R7 * Dimension DA 1 O(NW) memory O(2W) lookup R3 R4 R6 Dimension SA R5 R2 R1 R7

Grid-of-Tries 20K 2D rules: 2MB, 9 memory accesses (with prefix-expansion) Advantages Good solution for two dimensions Disadvantages Difficult to carry out updates Not easily extensible to more than two dimensions

Crossproducting [Sri98] 6 (8,4) 5 R2 R1 P1 4 R3 R4 (1,3) 3 2 1 1 2 3 4 5 6 7 8 9

Crossproducting Need: d 1-D lookups + 1 memory access, O(Nd) space 50 rules: 1.5MB, need caching (on-demand crossproducting) for bigger classifiers Advantages Fast accesses Suitable for multiple fields Disadvantages Large amount of memory Need caching for bigger classifiers (> 50 rules)

Outline Routing Lookups Packet Classification Motivation and problem definition Classification algorithms Linear search Associative search (TCAM) Trie-based techniques Crossproducting Tradeoffs in classification Heuristic algorithms References

Classification Algorithms: Speed vs. Storage Tradeoff Lower bounds for Point Location in N regions with d dimensions from Computational Geometry O(log N) time with O(Nd) storage, or O(logd-1N) time with O(N) storage . These bounds illustrate the difficulty of designing good PC algorithms. Even for small values of N and d, the storage or classification time becomes infeasible. N = 100, d = 4, Nd = 100 MBytes and logd-1N = 350 memory accesses

Classification Tradeoff in Hardware Switches/Routers Power consumption of classification subsystem Cost Speed Density (Storage)

Algorithms so far: Summary Good for two fields, but do not scale to more than two fields, OR Good for very small classifiers (< 50 rules) only, OR Have non-deterministic classification time, OR Either too slow or consume too much storage

One Solution: Heuristics that “seem to work well in real-life” Recursive Flow Classification [Gupta, McKeown 1999] Generalization of crossproducting to conserve storage Hierarchical Intelligent Cuttings [Gupta, McKeown 1999] Aggregated Bit-vector [Baboescu, Varghese 2001] Good heuristics do better than worst-case bounds for real-life datasets. Hierarchy (to at least some level) Structure Properties of real-life classifiers:

Lookup: What’s Used Out There? Overwhelming majority of routers: Modifications of multi-bit tries (h/w optimized trie algorithms) DRAM (sometimes SRAM) based, large number of routes (>0.25M) Parallelism required for speed/storage becomes an issue Others mostly TCAM based For smaller number of routes (256K) Used more frequently in L2/L3 switches Power and cost main bottlenecks

Classification: What’s Used Out There? Majority of hardware platforms: TCAMs High performance, cost, power, determinstic worst-case Some others: Modifications of RFC Low speed, low cost DRAM-based, heuristic Works well in software platforms Some others: nothing/linear search/simulated-parallel-search etc.

Packet Classification: References F. Baboescu and G. Varghese, “Scalable packet classification,” Proc. Sigcomm 2001 [Lak98] T.V. Lakshman. D. Stiliadis. “High speed policy based packet forwarding using efficient multi-dimensional range matching”, Sigcomm 1998, pp 191-202 [Sri98] V. Srinivasan, S. Suri, G. Varghese and M. Waldvogel. “Fast and scalable layer 4 switching”, Sigcomm 1998, pp 203-214 [Grid-of-tries, crossproducting] V. Srinivasan, G. Varghese, S. Suri. “Fast packet classification using tuple space search”, Sigcomm 1999, pp 135-146 P. Gupta, N. McKeown, “Packet classification using hierarchical intelligent cuttings,” Hot Interconnects VII, 1999 [Gupta99] P. Gupta, N. McKeown, “Packet classification on multiple fields,” Sigcomm 1999, pp 147-160 [RFC]

Packet Classification: References (contd.) P. Gupta, “Algorithms for routing lookups and packet classification”, PhD Thesis, Ch 1 and 4, Dec 2000, available at http://yuba.stanford.edu/ ~pankaj/phd.html [Background and introduction to Classification] P. Gupta and N. McKeown, “Algorithms for packet classification,” IEEE Network, March/April 2001, vol. 15, no. 2, pp 24-32 S. Iyer, R.R. Kompella, and A. Shelat, “ClassiPI: An architecture for fast and flexible packet classification,” IEEE Network, March/April 2001, vol. 15, no. 2, pp 33-41 TCAM vendors: netlogicmicro.com, sibercore.com, idt.com, cypress.com