Network Traffic Statistics Aggregation via Packet Group Identifiers
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Solution Overview
Problem
Current network testing methods face challenges in efficiently generating and analyzing test traffic to accurately measure network performance, particularly in managing and aggregating traffic statistics across multiple destination port units with limited memory capacity.
Innovation Solution
The method involves identifying tracking factors, generating global flow identifiers, creating a port-specific PGID map, and transmitting packets with PGIDs to compress and aggregate traffic statistics, allowing for efficient data collection and analysis across destination port units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traffic statistics are accumulated separately at each destination port unit, then measurement precision is improved, but device complexity increases due to multiple port units needing coordinated data management
Solution Approach 1:
The patent divides the network testing system into multiple independent port units, each capable of accumulating traffic statistics autonomously. Each port unit maintains its own packet group identifier map and flow statistics, eliminating the need for complex inter-port coordination while preserving measurement accuracy through distributed data collection
Solution Approach 2:
Each port unit is designed with universal functionality to handle multiple tracking factors and packet group identifiers simultaneously. The port units use a standardized data structure that can accommodate various traffic statistics requirements, allowing any port unit to serve multiple measurement purposes without requiring specialized configurations
2Measurement precision
If detailed traffic statistics are collected for each packet group, then measurement precision is improved, but loss of substance increases due to memory capacity constraints at destination port units
Solution Approach 1:
The patent extracts only the essential traffic statistics data from complete packet information. Instead of storing entire packet details, the system extracts and accumulates specific flow statistics such as packet counts, byte counts, and timing information. This extraction approach maintains measurement precision while dramatically reducing memory requirements at each port unit
Solution Approach 2:
The patent inverts the traditional approach by having port units accumulate statistics locally and then aggregating results centrally, rather than collecting all raw packet data and processing it centrally. This inversion reduces the data volume that needs to be stored and transmitted, preserving memory capacity while maintaining statistical accuracy
3Measurement precision
If multiple tracking factors are monitored simultaneously, then measurement precision is improved, but device complexity increases due to the need for multiple global flow identifiers
Solution Approach 1:
The patent implements a nested hierarchical structure for flow identifiers where packet group identifiers are nested within global flow identifiers. Each level of the hierarchy represents a different tracking factor, allowing multiple dimensions of traffic analysis to be monitored simultaneously through nested identifier relationships rather than separate independent systems
Solution Approach 2:
The patent combines multiple tracking factors into a unified flow identification system. Instead of maintaining separate counter systems for different traffic characteristics, the system merges source address, destination address, protocol type, and other tracking factors into a single integrated flow statistics accumulation mechanism, reducing device complexity while preserving analytical granularity
4Productivity
If traffic statistics are aggregated across all destination port units, then productivity is improved through centralized reporting, but loss of information increases due to data consolidation requirements
Solution Approach 1:
The patent performs preliminary aggregation of traffic statistics at each individual port unit before central consolidation. Each port unit pre-processes its accumulated statistics and prepares summarized data for transmission to the central testing system. This preliminary action reduces the volume of data requiring central processing while preserving port-specific characteristics through structured data formats that maintain source identification
Data Source
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AI summary
There are disclosed methods and apparatus for testing a network. A plurality of packets may be transmitted over the network from one or more source port units. Each transmitted packet may include a packet group identifier (PGID) corresponding to a unique combination of values for a plurality of tracking factors. The packets may be received at one or more destination port units. Each destination port unit may extract the PGID from each received packet and may accumulate traffic statistics for each of the plurality of PGIDs. The accumulated traffic statistics for at least some of the plurality of PGIDs may be aggregated to report summary statistics for a selected tracking factor.