Network Diagnostic Signature Optimization via Boolean Decision Trees
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Solution Overview
Problem
Conventional methods for network diagnostics face scalability challenges due to the increasing volume of network defect signatures, leading to inefficient data collection and processing loads, especially when dealing with large numbers of Diagnostic Signatures.
Innovation Solution
The proposed solution involves representing Diagnostic Signatures as sum-of-product Boolean functions, sorting commands by frequency of occurrence, and constructing a decision tree to determine a preferred sequence of command dispatch, minimizing resource usage and optimizing data collection by deferring unnecessary data retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to collect and process data for all Diagnostic Signatures, then complete diagnostic coverage is achieved, but processing time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing Diagnostic Signatures offline to generate optimized execution sequences and decision trees. This preliminary analysis determines the optimal order of command execution and identifies which data to collect based on predicted diagnostic outcomes, allowing the system to achieve complete diagnostic coverage without collecting all possible data during actual execution.
Solution Approach 2:
The system dynamically adapts the data collection process by using decision trees that branch based on intermediate diagnostic results. Instead of following a fixed sequence for all Diagnostic Signatures, the system adjusts the execution path in real-time, collecting only the data necessary to reach a diagnostic conclusion, thereby reducing processing time while maintaining reliability.
2Measurement precision
If all Diagnostic Signatures are evaluated with complete data collection, then diagnostic accuracy is maximized, but resource consumption increases
Solution Approach 1:
The system extracts only the essential data needed for accurate diagnosis by using decision trees to identify critical data points. Instead of collecting all possible data for every Diagnostic Signature, the system extracts and collects only those data elements that are necessary to reach an accurate diagnostic conclusion, thereby maintaining diagnostic accuracy while reducing resource consumption.
Solution Approach 2:
The system changes the parameters of data collection by adjusting the scope and depth of data gathering based on the specific diagnostic context. Using pre-processed Diagnostic Signatures and decision trees, the system dynamically determines which parameters to measure and to what precision, ensuring accurate diagnosis while optimizing resource usage by avoiding unnecessary data collection.
3Adaptability or versatility
If the volume of Diagnostic Signatures increases to cover more network defects, then diagnostic capability improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of evaluating multiple Diagnostic Signatures into manageable components through offline pre-processing. Each Diagnostic Signature is analyzed separately to generate optimized execution sequences and decision trees, which are then combined during runtime. This segmentation allows the system to handle increasing volumes of Diagnostic Signatures without proportionally increasing runtime complexity.
Solution Approach 2:
The system introduces an intermediary layer of pre-processed Diagnostic Signature representations and decision trees that mediate between the raw Diagnostic Signatures and the actual data collection process. This intermediary structure simplifies the interaction between multiple Diagnostic Signatures and the execution engine, reducing system complexity while maintaining enhanced diagnostic capability.
Data Source
AI summary
Present technology is directed to preferred processing and the verification of diagnostic signatures for a plurality of network defect. The disclosed optimization process is based on expressing each Diagnostic Signature as a minimal sum of product Boolean function of associated network commands, followed by ranking of each command reference in the product terms of the Boolean expression as well as each Boolean product terms of the SOP Boolean expressions, and constructing a decision tree based on the provided rankings to thereby determine a minimum set of commands along with an preferred command dispatch sequence for evaluating a Diagnostic Signature. Further aspects include the translation of both the optimization computation (interpretation of network conditions associated with a network defect) and the computed workflow (dispatch of the command) into a series of declarative rules that can be processed by a machine reasoning engine to thereby automate the optimization process.