Packet Classifier Compression via Directed Graph Minimization
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Solution Overview
Problem
TCAMs used in packet classification face limitations such as limited capacity, high power consumption, heat generation, and cost, due to the need for a large number of entries to represent rules, especially when rules are not in ternary format, leading to inefficiencies in routers.
Innovation Solution
A method to construct a packet classifier by representing rules as a directed graph, formulating minimization problems, solving these using dynamic programming, and combining solutions to reduce the number of TCAM entries required, thereby instantiating a reduced packet classifier in a content-addressable memory device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If TCAM entries are increased to represent rules in prefix format, then packet classification functionality is maintained, but hardware capacity requirements increase significantly
Solution Approach 1:
The patent merges multiple TCAM entries into a single compressed entry by representing packet classification rules as a directed acyclic graph (FDD), where shared prefixes and common decision paths are combined. This reduces the total number of entries needed while maintaining equivalent classification functionality through the graph structure that preserves all necessary rule relationships.
Solution Approach 2:
The patent creates a compressed representation (copy) of the packet classifier rules in FDD format that captures the essential classification logic without requiring literal copies of each rule entry. The FDD structure serves as a compact model that can be instantiated in TCAM with far fewer entries than the original rule set would require.
2Adaptability or versatility
If TCAM capacity is increased to store more rules, then packet classification capability improves, but power consumption and heat generation increase
Solution Approach 1:
By merging multiple individual TCAM entries into a reduced set of compressed entries through FDD representation, the patent decreases the total number of active memory cells required. This directly reduces the power consumption associated with TCAM operations while preserving the full packet classification capability through the structured graph representation.
3Adaptability or versatility
If TCAM entries are expanded to represent range rules, then rule flexibility is maintained, but the number of required entries increases exponentially
Solution Approach 1:
The patent segments the packet classification problem into a directed graph structure where each node represents a decision point and each edge represents a rule path. This segmentation allows range rules to be represented through the graph topology rather than requiring explicit expansion into multiple individual entries, maintaining flexibility while reducing complexity.
Solution Approach 2:
The patent combines multiple expanded range rule entries into a single FDD structure that captures the same flexibility through shared paths and nodes. The graph structure merges common prefixes and decision logic across multiple rules, reducing the total entry count while preserving the ability to match various packet ranges.
4Speed
If TCAM chips are used for packet classification, then processing speed is improved, but hardware cost increases
Solution Approach 1:
By reducing the number of TCAM entries needed through FDD compression, the patent decreases the total TCAM capacity required. This allows the use of smaller, less expensive TCAM chips while maintaining the high-speed processing capabilities of TCAM-based architecture, thereby reducing hardware cost without sacrificing performance.
Data Source
AI summary
A method is provided for constructing a packet classifier for a computer network system. The method includes: representing a set of rules for packet classification as a directed graph; formulating a plurality of minimization problems from the directed graph, where subgraphs extending from non-terminal nodes in the directed graph represent a minimization problem (i.e., a one-dimensional packet classifier); solving the plurality of minimization problems; combining solutions for the plurality of minimization problems to yield a reduced set of rules which form a reduced packet classifier; and instantiating the reduced packet classifier in a content-addressable memory device.


