Network Flow Analysis Using Effective Sampling Ratio
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Solution Overview
Problem
Network flow analysis in modern networks faces inaccuracies due to corrupted or error-prone sampling parameters, leading to errors in extrapolated results, which can be costly in terms of computing resources when new samples need to be collected.
Innovation Solution
Calculating an effective sampling ratio (ESR) and determining if it is an outlier compared to stored values, using historical ESR values for extrapolation when outliers are detected to maintain accuracy without discarding sampled packets and re-collecting new samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sampling parameters are used for extrapolation, then network flow analysis can be performed, but corrupted or error-prone sampling parameters lead to inaccurate extrapolated results
Solution Approach 1:
The system calculates the effective sampling ratio (ESR) and compares it against a predetermined threshold or historical range. When the ESR falls outside acceptable parameters (indicating corruption or errors), the system triggers a feedback mechanism to discard the problematic sampling parameters and request new samples, thereby maintaining measurement precision while accounting for parameter reliability issues
Solution Approach 2:
The system performs preliminary validation of sampling parameters by calculating the effective sampling ratio before using the parameters for extrapolation. This preliminary check identifies corrupted or error-prone parameters in advance, preventing them from compromising the accuracy of extrapolated results
2Measurement precision
If new samples are collected when sampling parameters are corrupted, then accuracy can be maintained, but computing resources are wasted due to re-collection
Solution Approach 1:
The system implements a feedback mechanism that monitors the effective sampling ratio and selectively triggers re-collection only when corruption or errors are detected. When sampling parameters are valid, the system proceeds with extrapolation without re-collection, thereby maintaining accuracy while optimizing computing resource efficiency
Solution Approach 2:
The system changes the approach based on parameter validation results: when the effective sampling ratio indicates corruption, it transitions to re-collection mode; when parameters are valid, it proceeds with extrapolation. This dynamic parameter-based decision-making maintains accuracy while avoiding unnecessary resource consumption
Data Source
AI summary
In an example network flow analysis, sample packets can be received from a stream of packets over a time interval from a network element. The sample packets can be aggregated into a subclass of packets. A sample pool size of the stream of packets in the time interval, a number of sample packets received over the time interval, and a number of sample packets aggregated into the subclass of packets can be determined. An effective sampling ratio (ESR) from the sample pool size of the stream of packets in the time interval and the number of sample packets received over the time interval can be calculated. An extrapolated flow for the subclass of sample packets can be calculated by multiplying the ESR by the number of sample packets aggregated into the subclass of packets.