Net Neutrality Testing via Spoofed Data Flow Comparison
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Solution Overview
Problem
Detecting violations of net neutrality in internet service provider networks is challenging due to the difficulty in identifying discrimination against certain users or services, which can take various forms and is hard to detect efficiently and accurately.
Innovation Solution
The system generates two types of data flows, unspoofed and spoofed, to compare performance metrics such as speed and packet loss, allowing for the determination of net neutrality status by measuring differences in these metrics, with the spoofed flow emulating specific data types like video streams to identify potential discrimination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used to detect net neutrality violations, then the system complexity remains low, but the detection accuracy and reliability are insufficient due to the difficulty in identifying discrimination against certain users or services
Solution Approach 1:
The system segments the detection process into multiple specialized components: flow generation modules that create test data flows, flow injection modules that introduce flows into the network, flow monitoring modules that track flow performance, and analysis modules that compare results. This segmentation enables accurate detection of net neutrality violations by dividing the complex detection task into manageable, specialized functions.
Solution Approach 2:
The system introduces intermediary test data flows (both unspoofed and spoofed flows) that act as mediators to detect discrimination. These intermediary flows are injected into the network and monitored to determine if the ISP treats different types of traffic differently, thereby enabling indirect detection of net neutrality violations without requiring direct access to ISP routing decisions.
2Reliability
If comprehensive monitoring of all network traffic is implemented to accurately detect discrimination, then the detection reliability improves, but the loss of time and computational resources increases significantly
Solution Approach 1:
The system performs preliminary actions by pre-generating test data flows with known characteristics before actual net neutrality testing. These pre-prepared flows (including both unspoofed and spoofed versions) are ready for immediate injection and comparison, eliminating the need for real-time flow analysis and significantly reducing detection time while maintaining reliability.
Solution Approach 2:
The system uses partial monitoring by focusing only on specific test flows that are injected into the network rather than monitoring all network traffic. This selective approach involves injecting and monitoring only the necessary test flows (unspoofed and spoofed flows) to detect discrimination, thereby reducing the time and computational resources required compared to comprehensive monitoring of all traffic.
3Adaptability or versatility
If multiple types of data flows are generated and monitored to identify discrimination patterns, then the detection capability improves, but the device complexity and operational difficulty increase
Solution Approach 1:
The system implements multi-functionality through unified flow management that handles both unspoofed and spoofed flows through the same injection and monitoring infrastructure. The flow generator, injector, and monitor are designed to handle multiple flow types universally, enabling the system to detect various forms of discrimination (blocking, throttling, prioritization) without requiring separate specialized systems for each detection type.
Data Source
AI summary
Described herein are systems and methods that may determine a net neutrality status. Server and agent of an internet network may each generate two flows of data that are transmitted/received by the server/agent, respectively. The first flow comprises an unspoofed data flow as may be transmitted over the network based on an HTTP GET command. The second flow may comprise a spoofed data flow. The second flow may emulate, or spoof, a video stream or other data flows that may be altered in the network. Server or agent may compare the first flow relative to the second flow to determine differences in performance data, from which a net neutrality status can be detected. Generally, after a measurement command is invoked, the system starts a downstream measurement. Then after completion of the downstream measurement, the system starts an upstream measurement.