Hybrid Port Range Encoding for Router Filter Optimization
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Solution Overview
Problem
Conventional routers face inefficiencies in storing and processing filter criteria, particularly when dealing with port ranges, which leads to increased resource usage and reduced performance as the number of subscribers and port ranges increases.
Innovation Solution
The technique encodes port ranges using a combination of identifiers for frequently occurring ranges and index values for dynamically learned ranges, allowing for efficient storage and retrieval through associative data structures and tree-structured representations, reducing the number of encoded values required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional routers store all port ranges individually in filtering rules, then filtering accuracy is maintained, but memory resources and processing time increase significantly as the number of subscribers and port ranges grows
Solution Approach 1:
The patent merges multiple individual port range entries into consolidated ranges. Instead of storing each port range separately, the system combines adjacent or overlapping port ranges into single entries, reducing the total number of stored filtering rules while maintaining the same filtering accuracy.
Solution Approach 2:
The system dynamically learns and adapts port range patterns from observed traffic. By monitoring frequently accessed port ranges, the system automatically identifies and consolidates these into compressed representations, allowing the filtering structure to evolve and optimize itself based on actual usage patterns.
2Reliability
If conventional routers store all port ranges individually in filtering rules, then complete port range coverage is maintained, but processing speed and performance decrease as data structures grow larger
Solution Approach 1:
By merging multiple individual port range specifications into consolidated range entries, the system reduces the total number of comparisons required during packet filtering. This consolidation maintains complete port range coverage while significantly improving processing speed by reducing the size of data structures that must be searched.
Solution Approach 2:
The system performs preliminary consolidation of port ranges during rule installation and updates, rather than processing individual ranges during packet filtering. This pre-processing step creates optimized data structures that enable faster lookup and matching operations during actual packet processing.
3Measurement precision
If routers apply interface-specific filters to each packet flow, then filtering precision is improved, but the complexity of filter management and resource consumption increase
Solution Approach 1:
The patent creates a universal port range compression mechanism that can be applied across all interfaces and filtering contexts. The same consolidation algorithms and data structures are used regardless of interface or filter type, simplifying management while maintaining the precision benefits of interface-specific filtering.
Data Source
AI summary
In general, techniques are described for encoding port ranges. In one example, a method includes generating an encoded value that represents a specified port range including a first element storing an identifier that identifies a frequently occurring port range stored in an associative data structure of most frequently occurring port ranges, a second element storing an index that represents a dynamically-learned port range specifying at least a part of the specified port range, the dynamically-learned port range represented in a tree-structure of dynamically-learned port ranges and identified by the index, and applying, by a forwarding plane of the computing device, one or more filters associated with the encoded value to a packet that specifies a port included in the specified port range.