[PPT] - Multi-dimensional Packet Classification Yadi Ma, Suman Banerjee PowerPoint Presentation

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A Smart Pre-Classifier to Reduce Power Consumption of TCAMs for Multi-dimensional Packet Classification

Yadi Ma, Suman Banerjee University of Wisconsin-Madison

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Packet classification

R Internet S1 S2 Subnet A Subnet B D From To Traffic type Action S1 D Port 80 Forward via L1 S2 D * Drop all traffic A B * Reserve 50 Mbps L1 L2 Classifier at Router R

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Definition

Packet classification: given a classifier, find the first (highest priority)

matching rule for each incoming packet

A classifier contains a set of rules ordered by priority
Our focus: n-tuple classification
Example classifier:
Given a packet header: (32.75.226.153, 198.35.180.5, 80,1040, UDP)

Rule # Source IP

Dest. IP

Source Port

Dest. Port

Protocol Action 1 * 10.112.*.* 5001 - 65535 * TCP deny 2 32.75.226.153 * * 1001 - 2000 UDP deny 3 199.36.184.* * 49152 - 65535 * UDP deny 4 * * * * * permit

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Packet classification schemes

Software-based schemes

– Tradeoff between memory usage and speed – Examples: HiCuts, HyperCuts, EffiCuts, etc

Hardware (TCAM)-based schemes

– Popular for high-throughput packet classification

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Problem Statement

TCAMs are power-hungry
Design a TCAM-based method that:

– Greatly reduces power consumption of TCAMs, especially for large classifiers – Uses commodity TCAMs – Is easy to implement

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Outline

Introduction and motivation Design of SmartPC

– Algorithms to manage two-stage classification

Evaluation methods and results Conclusion

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Packet classification system for SmartPC

Two-stage classification

– First stage: pre-classifier – Second stage: two parallel searches

Index TCAM (Pre-classifier entries) Match index Index SRAM TCAM (Classifier rules) Associated SRAM (priorities + actions) “General” blocks Priority resolution Action “Specific” block

How to build an efficient pre-classifier?

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Pre-classifier

How to build a pre-classifier?

– Built on two dimensions: source IP address and destination IP addresses – By expanding and combining two dimensional rules recursively

Also shuffle original rules into different

TCAM blocks accordingly

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Why 5d to 2d is a good choice?

Maximum number of overlapping rules in the two-dimensional space

Analyze more than 200 real classifiers ranging in

size from 3 to 15,181

Maximum number of overlapping rules is an order of magnitude smaller than classifier size.

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An example classifier containing 14 rules

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Same example classifier containing 14 rules

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SmartPC

2 1 2 3/4 5 6 7 8 9 10 11/12/13

Dst_addr Src_addr

P0 P1

0,1,5,6,8 P0,P1

TCAM

Pre-classifier

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28 28 28

SmartPC

1 2 3/4 5 6 7 8 9 10 11/12/13

Dst_addr Src_addr

P0 P1

0,1,5,6,8 2, 3,4,9,10 P0,P1

Specific blocks

TCAM

Pre-classifier

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29 29 29

SmartPC

1 2 3/4 5 6 7 8 9 10 11/12/13

Dst_addr Src_addr

P0 P1

0,1,5,6,8 2, 3,4,9,10 P0,P1

TCAM

Pre-classifier General block

7,11,12,13

Specific blocks

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Example: how to build a pre-classifier

1 2 3/4 5 6 7 8 9 10 11/12/13

P0

2

, 1, 5, 6 7 ,11,12,13 , 8

P1

, P1

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Example: how to build a pre-classifier

1 2 3/4 5 6 7 8 9 10 11/12/13

Dst_addr Src_addr

P0

P0 0 , 1, 5, 6 7 ,11,12,13 , 8

P1

2, 3,4,9,10 , P1

Specific blocks General block Pre-classifier

packet

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44 44 44 Index TCAM (Pre-classifier entries) Match index Incoming packet Index SRAM 0, 1, 5, 6, 8 7, 11, 12, 13 TCAM (Classifier rules) Associated SRAM (priorities + actions) General block(s) 1, accept Priority resolution accept 7, deny 1 1 P0 P1 2 ,3, 4, 9, 10 Specific block

. . .

Packet classification system for SmartPC

0, 1, 5, 6, 8 7, 11, 12, 13 1, accept 7, deny

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Properties of pre-classifiers

Entries in a pre-classifier are non-overlapping
Each rule in a classifier is either covered by only
ne pre-classifier entry, or marked as general

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Rule update

Rule update overhead of SmartPC is generally smaller

than that of regular TCAMs

The ordering of TCAM entries is kept within one specific

block or within a small number of general blocks, rather than throughout all the blocks

Rule update

– Insert a rule – Delete a rule

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Outline

Introduction and motivation Design of SmartPC

– Algorithms to manage two-stage classification

Evaluation methods and results Conclusion

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Experimental setup (1)

Summary of classifiers

Name Size MaxOveralps Wildcard S1 9802 22 4 S2 9416 126 57 S3 9497 76 18 S4 9624 82 12 S5 7255 28 S6 99823 27 5 S7 87039 249 79 S8 99836 89 47 S9 99866 81 38 S10 99220 10

10 real classifiers 10 synthetic classifiers

Name Size MaxOveralps Wildcard R1 5233 49 18 R2 5626 63 32 R3 5874 98 48 R4 6339 47 16 R5 7356 38 5 R6 8063 64 35 R7 8475 31 4 R8 10054 1 R9 11574 334 271 R10 15181 177 143

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Experimental setup (2)

Block size of TCAMs

– Evaluated various sizes: 32, 64, 128, 256, 512 and 1024, respectively.

Metric

– Power reductions

Percentage of reductions on activated blocks

– Storage overhead of pre-classifier entries

Percentage of pre-classifier size compared to the size of a whole

classifier

Schemes

– SmartPC – Default TCAM (without SmartPC) – A naïve scheme named Naive-divide

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Power reductions

With block size 128, the median and average power reductions are 91% and 88%, respectively

Real classifiers Synthetic classifiers

Percentage of power reductions vs. TCAM block size

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Storage overhead

Real classifiers Synthetic classifiers

Small storage overhead, less than 4% for every classifier.

Fraction of storage overhead vs. TCAM block size

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Comparison of SmartPC with Naïve-divide

Real classifiers Synthetic classifiers

SmartPC outperforms naïve-divide by more than 20% on average.

Percentage of power reductions with block size 128

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Discussion

Effect of prefix distribution and prefix length
Power reduction on small classifiers
Power reduction on IPv6 classifiers

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Conclusion

Uses commodity TCAMs Is easy to implement Greatly reduces power consumptions of TCAMs, especially for larger classifiers

Propose SmartPC, which:

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Questions

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Thanks

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Backup slides

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Prior work on Packet Classification

Software-based approaches

– Examples: HiCuts, HyperCuts, EffiCuts, etc

TCAM-based approaches

– High speed but suffer from some deficiencies such as high power consumption – Schemes for power efficiency:

CoolCAMs (INFOCOM 2003): reduce power consumption of

TCAMs, but limited to IP forwarding

Extended TCAMs (ICNP 2003): requires a new type of TCAM

that returns multiple matches

Significant recent work within companies and are of

proprietary nature

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Number of blocks activated vs. block size

R1 R9 S4 S10

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Observations

TCAMs

– The main component of power consumption in TCAMs

is proportional to the number of searched entries – Hardware supports turning on a small number of blocks – Hardware supports multiple searches simultaneously, such as Cisco’s TCAM4

Classifiers

– For each incoming packet, often only a small number of matching rules in a classifier need to be searched

http://www.cisco.com/en/US/prod/collateral/switches/ps5718/ps4324/prod_white_paper0900aecd806dc821.html

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Some stats

A 2006 report reported:

– Data centers in U.S. today consume about 61 billion kWh (1.5%

f total U.S. electricity consumption) for a total electricity cost of

about $4.5 billion – National energy consumption by servers and data centers could nearly double by 2011 to more than 100 billion kWh

According to a Sigcomm CCR 2008 paper, network

consumes 10-20% of a data center's total power.

With the growing sizes of classifiers, and the transition

from IPv4 to IPv6, the high power consumption of TCAMs increases both power supply cost and cooling cost

Report to Congress on Server and Data Center Energy Efficiency by U.S. Environmental Protection Agency. The cost of a cloud: research problems in data center networks in SIGCOMM CCR 2009

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Properties of real classifiers

Maximum number of overlapping rules in the two-dimensional space Number of wildcard rules in the two-dimensional space

Analyze more than 200 real classifiers ranging in size

from 3 to 15,181

Reduce the five-dimensional problem to two-dimensional!

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Pre-process a classifier

Given a mutlti-dimensional classifier C

containing a number of rules:

– The two-dimensional space is divided into non-

verlapping rectangles. Each rectangle covers a

cluster of rules and represents an entry in the pre- classifier P for C – Shuffle rules in C such that each pre-classifier entry is associated with a TCAM block, named a specific block – If the number of rules that intercept with a pre- classifier entry exceeds TCAM block size, those extra rules are stored in TCAM blocks named general block(s)

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2, 3, 4, 16 5, 6, 7, 8, 9 11, 12, 13, 14, 15

Dst_addr Src_addr

Given a classifier which contains 19 rules, block size = 5

1 2 3 4 5 7 8 9 6 10 13 11 14 12 15 19

P1 P2 P3

16 17 18

1, 10, 17, 18, 19

Pre-process a classifier

2-dimensional pre-classifiers entries In TCAM block(s) 5-dimensional classifier rules in TCAM blocks

Specific blocks General blocks

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Result Key Expect huge power reduction on large classifiers

Pre-classifier

TCAM