Application Problem Signature Extraction Using Trace Data
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Solution Overview
Problem
Identifying and troubleshooting network communication issues in software and hardware applications can be challenging due to the complexity of multiple potential failures, making it difficult to determine the cause and implement fixes.
Innovation Solution
A problem signature extraction technique that uses network traffic features to create compact signatures for application problems, allowing for the recognition of new issues as matching previously seen problems, employing a learning-based approach with coarse-grained labels to build classification trees and sub-classify failures without specific labels, and using these signatures to determine fixes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of network traces is performed to identify problems, then accuracy in problem identification can be maintained, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system automatically generates problem signatures from network traces without requiring manual analysis. The signature generation process self-services by extracting key characteristics and patterns from the trace data, enabling automated problem identification and matching against known issues, thereby reducing both time and operational complexity while maintaining accuracy
Solution Approach 2:
The system creates simplified copies of complex network problems in the form of problem signatures. These signatures capture the essential characteristics of problems without containing the full complexity of the original traces, enabling rapid comparison and matching against a database of known problems while preserving the ability to accurately identify the root cause
2Measurement precision
If detailed analysis of all network trace features is performed to accurately classify problems, then problem classification accuracy improves, but system complexity and computational resources increase
Solution Approach 1:
The system extracts only the most relevant features from network traces to create problem signatures. By selecting and extracting key characteristics such as packet patterns, protocol behaviors, and error indicators, the system achieves accurate problem classification without processing all trace features, thereby reducing computational complexity and resource requirements
Solution Approach 2:
The problem classification process is segmented into distinct stages: trace processing, signature generation, and problem matching. Each stage handles specific aspects of the analysis independently, allowing the system to achieve high classification accuracy through modular processing rather than requiring a single complex analysis system
3Reliability
If comprehensive problem databases with detailed solutions are maintained, then solution accuracy and completeness improve, but storage requirements and search complexity increase
Solution Approach 1:
Instead of storing complete problem databases with all possible network traces and solutions, the system maintains a database of condensed problem signatures. These signatures serve as compact representations that capture the essential characteristics of known problems, enabling reliable solution matching with significantly reduced storage requirements and faster search performance
Data Source
AI summary
The problem signature extraction technique extracts problem signatures from trace data collected from an application. The technique condenses the manifestation of a network, software or hardware problem into a compact signature, which could then be used to identify instances of the same problem in other trace data. For a network configuration, the technique uses as input a network-level packet trace of an application's communication and extracts from it a set of features. During the training phase, each application run is manually labeled as GOOD or BAD, depending on whether the run was successful or not. The technique then employs a learning technique to build a classification tree not only to distinguish between GOOD and BAD runs but to also sub-classify the BAD runs into different classes of failures. Once a classification tree has been learned, problem signatures are extracted by walking the tree, from the root to each leaf.