1 Space-Efficient TCAM-based Classification Using Gray Coding Anat Bremler – Barr...

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Space-Efficient TCAM-based Classification Using Gray Coding

Anat Bremler – Barr

Interdisciplianry Center

Danny Hendler

Ben-Gurion University

Infocom paper presentation

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Talk outline

Definitions Problem definition, prior art The Short Range Gray Encoding Algorithm Experimental results Future work

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Packet Classification

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Packet Classification

ACL IDACL ID Sourceaddr

Sourceaddr

Sourceport

Sourceport

Destaddr

Destaddr

Destport

Destport ProtocolProtocol ActionAction

ACL11ACL11 128.32.0.0128.32.0.0 8080 32.*.*.*32.*.*.* 8080 TCPTCP AllowAllow

ACL11ACL11 127.*.*.*127.*.*.* 34-3634-36 32.12.1.132.12.1.1 8080 UPDUPD AllowAllow

ACL11ACL11 128.32.0.0128.32.0.0 ≤ 1024≤ 1024 95.12.3.395.12.3.3 ≤ 1024≤ 1024 TCPTCP DenyDeny

ACL11ACL11 117.57.3.2117.57.3.2 5555 46.2.67.1146.2.67.11 1515 UDPUDP LogLog

ACL11ACL11 117.57.3.2117.57.3.2 136136 32.*.*.*32.*.*.* 2525 TCPTCP DenyDeny

ACL11ACL11 95.14.5.195.14.5.1 >1024>1024 32.12.1.132.12.1.1 15-1815-18 TCPTCP AllowAllow

ACL11ACL11 128.32.0.0128.32.0.0 >1024>1024 32.12.1.132.12.1.1 8080 TCPTCP LogLog

ACL database

f

header

payload

searchkey

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Ternary content-addressable memory

0011101101010XX00X01001111XXXX

11X00X00001110X0X101000110XXXX10XX010100X0XX0100011010X01000

001110XXXXXXXXXXXXXXXXXXXXXXX

.

.

.

1110XX010X01X0010101010X0XXXXX

TCAM

• Associative memory: parallel comparisons against all entries

• Fixed-width entries

• Ternary digits: 0 / 1 / X (don’t care)

• Only first match is returned

Width of W digits

0011101101010000010100111110110

Search key

1

2

3

4

1

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TCAM: pros, cons, usage

Pros

• High throughput

• Deterministic throughput

Cons

• Higher cost (~X30 than SRAM)

• Higher power consumption0011101101010XX00X01001111XXXX

11X00X00001110X0X101000110XXXX10XX010100X0XX0100011010X01000

001110XXXXXXXXXXXXXXXXXXXXXXX

.

.

.

1110XX010X01X0010101010X0XXXXX

1

2

3

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TCAM

Usage

• Over 6M deployed devices (2004)

• Used in multi-gigabit systems with >10K rules

• May support 128K entries of 144- bit, 133M searches/second.

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The problem: TCAM range representation

001110110110110000000

Match-type rule field value matching key-fieldexact

prefix

range

00111011011011000000001110110110110000000001*****************

>1024 2012

How can we efficiently represent range rules by TCAM entries?

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Basic approach: prefix expansion

Representing [1,6]

TCAM entries:

001 ,01 ,*10 ,*110

Prefix expansion is inefficient • A range over W-bits may expand to 2W-2 entries

• For 2 range-fields, may expand to (2W-2)2

• Expansion factor of up to 6 on real-world databases !!!

010

0 1

000 001 011 100 101 110 111

[1,6]

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Prior art: use of extra bits

0011101101010XX00X01001111XXXX

11X00X00001110X0X101000110XXXX10XX010100X0XX0100011010X01000

001110XXXXXXXXXXXXXXXXXXXXXXX

.

.

.

1110XX010X01X0010101010X0XXXXX

1

2

3

4

TCAM

XXXXX

XXXXXXXXXX

XXXXX

.

.

.

XXXXX

Extra bits(typically 36)

• Hierarchical database dependent encoding [Liu2002], [Lunteren and Engbersen2003]

• Database-Independent Range Pre-Encoding [Venkatachary,Lakshminarayanan, Rangarajan2005]

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Prior art: database-dependent encoding

Key idea: allocate an extra bit to commonly occurring ranges.

Example

Source-port ≥ 1024

0011101101010XX00X01001111XXXX

11X00X00001110X0X101000110XXXX10XX010100X0XX0100011010X01000

001110XXXXXXXXXXXXXXXXXXXXXXX

.

.

.

1110XX010X01X0010101010X0XXXXX

1

2

3

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TCAM

XXXXX

XXXXXXXXXX

XXXXX

.

.

.

XXXXX

Representing a rule

Set the assigned extra bit to 1

Set all other extra bits to X

11010010101XXXXXXXXXXXXXXXXXX 1

Generating the search key

If source-port within range set extra bit to 1

Otherwise set extra bit to 0

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Prior art: database-independent range –pre-encoding (DIRPE)

Key idea: Use extra bits for independent encoding, use general ternary values rather than prefixes.

Fence encoding (w-bit words)

Range Encoding =i 02w-i-11i

≥i x2w-i-11i

<i 02w-ixi-1

[i,j] 02w-1-jXj-i1i

Fence encoding

•Expansion 1

•Requires 2w-1 bits

What if we have a smaller number of bits?

Number i is encoded by: 02w-1-i1i

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XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

W+36 bits

Key idea: Divide all (regular plus extra) bits to chunks, encode each by fence encoding

Prior art: database-independent range –pre-encoding (cont’d)

What if a smaller number of bits is available?

Chunk1 (k1 bits)

Chunk2 (k2 bits)

Chunk3 (k3 bits)

Chunk4 (k4 bits)

Range expansion increases with the number of chunks

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An Observation: The problem is equivalent to the DNF expression minimization problem

R=[10,11]

b1b0 + b1b’0 ≈ b1

The general problem is NP-complete.

“Computing the minimum DNF representation of boolean functions defined by interval”

[Schieber, Geist, Zacks, 2005]

• A linear-time algorithm for finding minimum-size DNF expression for any range of binary-coded numbers• Worst-case expansion for binary-encoded numbers is 2W-4

Thanks to Ronny Roth for the observation and the reference to the paper

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Talk outline

Definitions Problem definition, prior art The Short Range Gray Encoding Algorithm Experimental results Open questions

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Our solution: Short-Range Gray Encoding (SRGE)

Hybrid-SRGE yields range-expansion of only 1.02 on real databases

Gain without pain: Range expansion reduction can be obtained without the use of extra bits by changing the encoding scheme (SRGE)

SRGE can be combined with database-dependent scheme: the Hybrid-SRGE scheme

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Our solution: observations

1. Ranges tend to be small: typically correspond to similar-functionality ports: 161-162: snmp, snmptrap 67-68: bootps server, bootps client 2300-2400: Microsoft DirectX

2. Binary coding not optimal for small ranges

1000 01 11

Binary encoding

An example: covering [1,2]

Cover set: {01, 10}

1100 01 10

Gray encoding

Cover set: {*1}

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000 001 011 010 110 111 101 1003-bit BRGC: 000 001 011 010 110 111 101 100

Binary Reflected Gray Code

Obviously, not `our’ Frank

Gray

Gray code: codewords for consecutive integers differ by single bit

4-bit BRGC: 0 0 0 0 0 0 0 0

100 101 111 110 010 011 001 0001 1 1 1 1 1 1 1

Transforming binary BRGC is quick

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Binary Reflected Gray Code (cont’d)

01

01 1 0 1 1 00 1

0 10

000 001 011 010 110 111 101 100

It is exactly this reflection property that helps decrease expansion

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The SRGE algorithm

Need to find minimum cover of ]s,e[ using gray coding.

s e

p

Find the least common ancestor p of point s and e

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Let pl be the rightmost leaf in p’s left sub-tree

Let pr be the leftmost leaf in p’s right sub-tree

s epl pr

p

The SRGE algorithm

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First, we handle the smaller of: ]s,pl[, ]e,pr[

s epl pr

p

The SRGE algorithm

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prpl

Cover by prefixes the smaller range and its mirror relative to p

We still need to cover the leftover range ]s’,e[, if it is non-empty

The SRGE algorithm

s e

p

s’

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s' e pl’ pr’

Repeat the previous procedure for the leftover: [s',e]• find their least common ancestor p’• let pl' be the rightmost leaf in the left sub-tree of p'• let pr' be the leftmost leaf in the right sub-tree of p'

p’

p

pr s

The SRGE algorithm

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Two cases to consider: 1) |[pr', e]| > |[s', pl']|:

Cover [pr', e] by prefixes The mirror of [pr', e] (relative to p') covers [s', pl']

pr s’ pl’ pr’ e

p’

p

s

The SRGE algorithm

25pr s’ pl’ pr’ e

2) |[s', pl']|>|[pr', e']|:

Cover [pr', e] by prefixes. Cover [s', pl'] by one a single prefix, corresponding to p' left

sub-tree

p’

p

s

ql

q

The SRGE algorithm

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Hybrid-SRGE

• For each unique range, compute total number of redundant entries under SRGE

• Deal with the most expensive ranges by using standard database-dependent encoding

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Talk outline

Definitions Problem definition, prior art The Short Range Gray Encoding Algorithm Experimental results Future work

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SRGE range-expansion reduction

Random ranges

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Results on a real-life database 223K rules with 300 unique ranges Combined from collection of 126 separate databases

(firewall, acl-routers, intrusion prevention systems)

Hybrid SRGE

Acknowledgment: Cisco, David Taylor (WHSTL)

Algorithm

Expansion

1.03 1.2

Redundancy

Hybrid DIRPE 1.12 NA

Prefix expansion 2.6 NA

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Range-length distribution

Almost 60% of the unique rangeshave length less then 20

Approx. 40% of the total number of ranges have length less then 20

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A small number of ranges cause most expansion

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Range expansion bounds

The worst-case expansion ratio of SRGE on w-bit words is 2w-4

The worst-case expansion ratio of any range-covering scheme on w-bit words is at least w, regardless of the encoding scheme

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Expansion as function of bits number

2w-2

2^w-1w1

Number of bits used

Nu

mb

er o

f T

CA

M e

ntr

ies

At least W entries required – regardless of the encoding technique

SRGE worst-case expansion is 2W-4 entries

Unknown