Diagnostic Identifier Generation for Cloud Request Tracing
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Solution Overview
Problem
Debugging errors in computer programs, especially when multiple applications across different computing environments interact, is challenging due to compatibility issues and the complexity of preserving and analyzing stack trace data, especially in cloud computing environments where hardware is disembodied from developers.
Innovation Solution
Generating diagnostic identifiers based on parameter data from messages using hash operations, allowing for comparison of related requests and identifying similarities or errors across different requests in a cloud computer system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stack trace data is preserved for each application to trace errors, then error tracing capability is improved, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent extracts only the essential diagnostic information (parameter data) from complete stack trace data to create simplified diagnostic identifiers. This extraction process removes unnecessary complexity while retaining the core information needed for error tracing and correlation across applications.
Solution Approach 2:
The patent applies different levels of detail to different parts of the diagnostic data structure. Complete stack traces are maintained locally where needed for detailed analysis, while simplified diagnostic identifiers are used for cross-application correlation and high-level tracing, optimizing both detail and efficiency.
2Reliability
If diagnostic information is generated for multiple applications across different computing environments, then error tracing across distributed systems is improved, but compatibility issues and analysis difficulty worsen
Solution Approach 1:
The patent creates a universal diagnostic identifier format that can represent parameter data from multiple different applications and computing environments in a standardized way. This universal format enables cross-application correlation without being tied to any single application's specific data structure or format.
Solution Approach 2:
The patent transforms diverse parameter data from different applications into a standardized diagnostic identifier format by applying hash operations and structured encoding. This parameter transformation enables compatibility across different computing environments while preserving the essential diagnostic information.
3Measurement precision
If complete stack trace data is preserved and analyzed, then error identification accuracy is improved, but processing time and resource consumption increase
Solution Approach 1:
The patent segments diagnostic information into two parts: simplified diagnostic identifiers for rapid correlation and comparison across applications, and complete stack trace data preserved locally for detailed analysis when needed. This segmentation enables fast initial error identification followed by targeted deep analysis only when necessary.
Solution Approach 2:
The patent performs partial analysis by first comparing simplified diagnostic identifiers to quickly identify potential errors and correlations. Only when these partial results indicate actual issues do the system perform the more resource-intensive analysis of complete stack trace data, avoiding unnecessary full analysis in normal operation.
Data Source
AI summary
Techniques are described for determining diagnostic information (e.g., a diagnostic identifier) for one or more requests in a cloud computer system. A diagnostic identifier may be generated based on parameter data corresponding to one or more parameters in a request. A computing operation may be performed to generate a code (e.g., a hash code) for each parameter in the parameter data. The generated code(s) may be combined to form a diagnostic identifier to identify the parameter data. A comparison may be performed by comparing each code in one diagnostic identifier to a code in another diagnostic identifier corresponding to a parameter for which the codes were generated. A measure of a match between the two identifiers may be determined using a result of the comparison. The measure of the match may enable tracing and analysis of one or more requests in the cloud computer system.