090003: A Systematic Approach to Minimizing Packet Classifiers in TCAMs
Case ID:
TEC2009-0003
Web Published:
7/21/2014
Description:
Packet classification enables many networking
services on the Internet, such as firewall packet filtering and traffic
accounting. Using Ternary Content Addressable Memory (TCAM) chips to perform
high-speed packet classification has become the de facto standard in industry.
TCAM chips classify packets by comparing a packet with the full suite of
classification rules in ternary encoding, the comparison being done in
parallel.
Unfortunately, TCAMs have limitations of
relatively small capacity, high power consumption & heat generation, and
high cost. The well-known, range-expansion problem exacerbates these limitations
by significantly decreasing the limited capacity of TCAMs as each classifier
rule typically has to be converted into multiple TCAM rules.
Traditional packet classification looks at
five fields: source and destination IP addresses, source and destination port
numbers, and protocol type. New Internet services and new security threats are
leading to more complex rule sets. Along with the increasing adoption of IPv6,
the size and width growth of packet classifiers puts more demand on TCAM
capacity.
Description
Michigan State University has
developed a suite of algorithms to enable the optimal use of TCAM
chips. This algorithm takes a given packet classifier as an input and then
outputs a semantically equivalent packet classifier that requires a minimized
number of TCAM entries. TCAM Razor uses decision diagrams and dynamic
programming to minimize the required chip size, thus decreasing the total cost
and power requirements for TCAM chips.
Other
inventions inventions in the suite include:
* A
Systematic Non-Prefix Approach to Minimizing Packet Classifiers in
TCAMs (100004)
* Efficient TCAM-Based
Packet Classification Using Multiple Lookups and Claissifier Semantics
(100005)
IP Protection
Status
Patent pending
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For Information, Contact:
Raymond Devito
Technology Manager
Michigan State University - Test
517-355-2186
devitora@msu.edu