Backbone Traffic Flow Attribution Without Duplicate Counting
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Solution Overview
Problem
Conventional approaches to managing backbone networks are limited by the unavailability of flow and usage data, making it difficult to optimize and determine issues impacting performance or usage, as flow data is often not collected from all devices due to load constraints.
Innovation Solution
An algorithm is applied to backbone devices to prevent duplicate counting of traffic flow data without analyzing content, using rules based on source and destination addresses and region information, with a flow manager collecting and attributing traffic to services or entities, and utilizing switch pathing for path information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If flow data is collected from all backbone network devices, then measurement precision and network optimization capability are improved, but device complexity and processing load increase
Solution Approach 1:
The patent introduces a flow manager as an intermediary component that centralizes the collection, aggregation, and processing of flow data from multiple backbone network devices. This mediator handles the complexity of data normalization, duplicate detection, and correlation, allowing individual network devices to maintain simpler configurations while achieving comprehensive network-wide flow data accuracy through centralized intelligence.
2Loss of information
If flow data is collected from all devices, then network visibility and optimization are improved, but processing load and resource consumption increase
Solution Approach 1:
The patent merges flow data from multiple backbone network devices into a unified data structure at the flow manager, consolidating redundant information and eliminating duplicates through correlation algorithms. This combining approach reduces the total volume of data that requires intensive processing, as overlapping flow records from different devices are identified and merged into single representative records, thereby reducing overall processing load while maintaining complete network visibility.
Solution Approach 2:
The system transforms raw flow data into normalized parameters with standardized formats, time windows, and aggregation levels. By changing the parameter representation of flow data into compact, standardized forms, the system reduces processing complexity and resource consumption while preserving all essential information needed for network optimization and analysis.
3Quantity of substance
If duplicate flow data is not prevented, then data completeness is improved, but data accuracy and reliability deteriorate due to double-counting
Solution Approach 1:
The flow manager implements feedback mechanisms that continuously monitor incoming flow data for duplicates by comparing against previously processed records. When duplicate flows are detected, the system provides feedback to suppress or merge these redundant records, ensuring that each unique traffic flow is counted exactly once. This feedback-driven duplicate prevention maintains data reliability while preserving complete flow information.
Data Source
AI summary
Traffic flow across a backbone network can be determined even though flow data may not be available from all network devices. Flow data can be observed using types of backbone devices, such as aggregation and transit devices. An algorithm can be applied to determine which data to utilize for flow analysis, where this algorithm can be based at least in part upon rules to prevent duplicate accounting of traffic being observed by multiple devices in the backbone network. Such an algorithm can use information such as source address, destination address, and region information to determine which flow data to utilize. In some embodiments, address mapping may be used to attribute this traffic to various services or entities. The data can then be analyzed to provide information about the flow of traffic across the backbone network, which can be useful for purposes such as network optimization and usage allocation.