Dynamic Log Prioritization for Network Transmission Efficiency
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Solution Overview
Problem
Conventional log collection methods in deduplicating file systems result in support bundles containing irrelevant data, leading to increased transfer times, storage requirements, and reduced efficiency in issue resolution.
Innovation Solution
An automated system that analyzes the filesystem service's process memory at the time of a crash, identifies relevant files using thread relationship rules and hierarchical thread trees, and filters them based on priority weights to create a compact support bundle.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional predefined list methods are used to collect all possible log files, then comprehensive data coverage is achieved, but support bundle size increases significantly
Solution Approach 1:
The patent extracts only the relevant log files needed for debugging from the complete set of available logs. By analyzing the crash context and identifying which specific logs are necessary, the system removes unnecessary data while retaining critical information, thus reducing bundle size without sacrificing diagnostic value
Solution Approach 2:
The patent segments the log collection process into multiple stages: initial crash detection, relevance analysis based on crash context, selective log file identification, and prioritized collection. This segmentation allows the system to collect only essential logs rather than all possible logs, optimizing the balance between completeness and size
2Loss of information
If all log files are collected without size constraints, then complete debugging information is gathered, but transfer time to support increases
Solution Approach 1:
The system extracts and prioritizes only the most relevant log files based on crash analysis, removing unnecessary logs from the collection. This extraction process ensures that the essential debugging information is retained while significantly reducing the total data volume that needs to be transferred
Solution Approach 2:
The patent performs preliminary analysis of the crash context and pre-identifies which log files are relevant before the actual collection process. This preliminary action allows the system to prepare a focused list of necessary logs, avoiding the need to transfer unnecessary data and reducing overall transfer time
3Loss of information
If manual support bundle creation is used to include only relevant files, then data relevance is improved, but time to create the bundle increases
Solution Approach 1:
The patent implements an automated system that performs crash analysis, identifies relevant log files, and creates the support bundle without human intervention. The system serves itself by automatically analyzing the crash context, determining log relevance, and executing the collection process, thereby achieving both high data relevance and rapid bundle creation
Solution Approach 2:
The system uses feedback from crash analysis results to dynamically determine which log files are relevant. By analyzing the crash context and using this information to guide log selection, the system automatically adjusts the collection process to include only necessary files, achieving manual-quality relevance with automated speed
4Productivity
If automatic support bundle transmission is implemented, then faster reporting to support is achieved, but file size limitations are encountered
Solution Approach 1:
The patent implements a dynamic log selection process that adapts to transmission constraints. The system prioritizes log files based on relevance to the crash and dynamically adjusts the collection scope to fit within size limitations, ensuring that the most critical information is transmitted first while maintaining automatic transmission capabilities
Solution Approach 2:
The system changes the parameter of log file selection based on size constraints. By adjusting which logs are included and in what quantity, the system optimizes the bundle to fit transmission limits while maintaining automatic operation, thus achieving both fast reporting and compliance with size restrictions
5Loss of information
If excessive data is collected in support bundles, then more information is available for analysis, but storage infrastructure requirements increase
Solution Approach 1:
The patent extracts only the essential log files needed for debugging from the complete set of available logs. By using crash context analysis to identify and extract only relevant data, the system reduces the volume of information stored on support infrastructure while maintaining complete diagnostic capability
Solution Approach 2:
The system discards unnecessary log data that would consume storage space without providing diagnostic value. By selectively excluding irrelevant logs based on crash analysis, the system recovers storage resources while preserving all information necessary for effective debugging and analysis
Data Source
AI summary
Systems and methods for on-site filtering and prioritizing logged data prior to transmission to a support server are described. An analyzer application executing on the same system as a filesystem application may retrieve a core file dump in response to an unexpected behavior. An actively running thread at the time of the unexpected behavior may be identified, and thread relationship rules are used to generate a list of related threads based on sharing a common resource with the actively running thread. A relevance factor may be determined for each of the sub-components of the related threads based on a positioning within a hierarchical tree of the related threads. Log files from the sub-components may then be selected for a support file bundle, based on the relevance factors and the file sizes of the log files fitting within a size limit, then transmitted to the support server.