Application Level Test Packet Generation from Live Traffic Metadata
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Solution Overview
Problem
Current methods for testing network infrastructures struggle to accurately replicate real-world network traffic in test environments, leading to inaccurate representations and scalability issues due to the capture of extraneous packets and large data volumes.
Innovation Solution
Monitoring live network traffic to collect meta-data, extracting application-level meta-data, and generating test packets based on this data for a test network infrastructure, allowing for segmented data processing across time slots and improved visibility into application-level interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If direct packet capture technique is used to replicate real world traffic in test environment, then visibility into application level interactions is improved, but data storage requirements and system complexity increase significantly due to capturing extremely large numbers of packets
Solution Approach 1:
The patent extracts only the essential application-level metadata from captured packets, separating the useful information (application identifiers, traffic patterns, metadata) from the bulk packet data. This extraction process retains visibility into application interactions while dramatically reducing storage requirements by not storing complete packet captures.
Solution Approach 2:
Instead of copying and storing actual packet data, the system creates simplified metadata representations that capture the essential characteristics of application-level traffic. These metadata copies preserve the necessary information for testing purposes while occupying minimal storage space.
2Measurement precision
If direct packet capture technique is used to capture real world traffic, then accuracy of traffic representation is improved, but scalability deteriorates due to the extremely large numbers of packets that must be captured and processed
Solution Approach 1:
The system extracts only the critical metadata elements needed for accurate traffic representation, eliminating the need to process and store complete packet data. This extraction approach maintains measurement precision for application-level characteristics while dramatically improving scalability by reducing processing overhead.
Solution Approach 2:
The patent segments the traffic analysis process into metadata extraction and packet processing components, allowing the system to handle large volumes of traffic by processing only the essential metadata rather than complete packet streams, thereby improving scalability.
3Loss of information
If direct packet capture technique is used to capture all network traffic, then completeness of traffic data is improved, but device complexity increases due to handling extraneous and irrelevant packets
Solution Approach 1:
The system extracts only the relevant application-level metadata from network traffic, filtering out extraneous and irrelevant packet data. This extraction process maintains completeness of application-level information while reducing system complexity by eliminating the need to process and manage complete packet captures.
Solution Approach 2:
The patent segments the traffic data into essential metadata components and extraneous packet data, processing only the metadata portion. This segmentation reduces device complexity by focusing computational resources on the essential application-level information while discarding irrelevant packet details.
4Productivity
If application level meta-data is segmented into multiple data segments for different time periods, then processing efficiency is improved, but system complexity increases due to time slot management
Solution Approach 1:
The patent segments application-level metadata into time-based segments, allowing parallel processing of different time periods. This segmentation improves processing efficiency by enabling concurrent handling of multiple time slots while the modular structure manages the complexity through standardized processing routines.
Solution Approach 2:
The system dynamically adjusts time slot configurations and segmentations based on traffic patterns and processing requirements. This dynamic approach optimizes processing efficiency while adapting to varying complexity demands, allowing the system to scale processing granularity as needed.
Data Source
AI summary
Systems and methods are disclosed to recreate real world application level test packets for network testing. Live network traffic is monitored within a live network infrastructure, and live traffic meta-data is then collected for this live traffic. Application level meta-data is then extracted from the live traffic meta-data and stored in one or more data storage systems. Subsequently, the application level meta-data is received from the one or more data storage systems, and application level test packets for network testing are then generated based upon the application level meta-data. Further, application level meta-data collected during a time slot can be segmented in multiple different data segments associated with different time periods within the time slot, and application level test packets can be generated using these different data segments. Further, the live traffic meta-data collection can occur within multiple time slots.