Client Computing System Diagnostic Data Collection for Application Error Resolution
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Solution Overview
Problem
Current computing systems face challenges in quickly diagnosing and resolving issues with applications due to insufficient information available for support personnel, leading to trial and error processes and inefficient communication between users and support engineers.
Innovation Solution
A client computing system detects problems, identifies problem-specific diagnostic data, runs relevant analyzers, and packages the data for transmission to a service computing system to identify remedial actions, which are then surfaced to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If general log information is collected when a user encounters an error condition, then some diagnostic data is available, but the information is insufficient to quickly discover the root cause of the issue
Solution Approach 1:
The system performs preliminary actions by automatically collecting problem-specific diagnostic data and executing relevant analyzers at the moment a problem is detected, rather than waiting for support personnel to request data later. This preliminary data collection includes gathering application state information, configuration data, and running diagnostic analyzers before the user even contacts support, thereby having ready-to-use diagnostic information that eliminates the time loss associated with later data collection.
Solution Approach 2:
The system implements self-service by automatically detecting problems, identifying what diagnostic data is needed, collecting that specific data, and executing relevant analyzers without human intervention. The client computing system serves itself by autonomously gathering comprehensive diagnostic information and preparing it for transmission to support personnel, eliminating the need for support engineers to manually guide users through data collection processes.
2Ease of operation
If data collection tools are downloaded and run after the user has encountered the problem, then diagnostic data can be collected, but this creates extensive back and forth traffic between user and support person
Solution Approach 1:
The system performs all necessary data collection and analyzer execution as preliminary actions immediately when a problem is detected, before any support interaction occurs. The client computing system proactively gathers problem-specific diagnostic data, executes relevant analyzers, and prepares comprehensive diagnostic information in advance, so that when support personnel receive the issue, all necessary data is already available, eliminating the need for back-and-forth communication for data collection.
Solution Approach 2:
The system extracts and transmits only the specific diagnostic data and analyzer results relevant to the detected problem, rather than requiring support personnel to guide users through comprehensive data collection tools. By extracting only the necessary problem-specific information and preparing it in advance, the system eliminates the extensive back-and-forth traffic that would otherwise be needed to collect and transmit diagnostic data.
3Measurement precision
If support personnel manually analyze general log information, then they can attempt to diagnose the issue, but they do not have enough information to quickly discover the root cause
Solution Approach 1:
The system applies local quality by collecting and analyzing problem-specific diagnostic data tailored to the particular issue detected, rather than relying on general log information. Different types of problems trigger collection of different specific data sets and execution of different relevant analyzers, ensuring that support personnel receive precisely the diagnostic information needed for each specific problem type, thereby improving both diagnosis accuracy and resolution speed.
Solution Approach 2:
The system performs self-service diagnostic analysis by automatically executing relevant analyzers and generating diagnostic reports before support personnel are involved. The client computing system autonomously analyzes the collected diagnostic data, runs appropriate analyzers, and prepares comprehensive findings, so that support personnel receive pre-analyzed diagnostic information rather than raw logs, dramatically improving both accuracy and speed of problem resolution.
Data Source
AI summary
A client computing system detects when a problem is encountered with an application and identified problem-specific diagnostic data that is to be collected, given the detected problem. It also identifies one or more problem-specific test or diagnostic analyzers and executes those analyzers to generate additional data. The data that is collected and the data that is generated by the analyzers is packaged and sent to a service computing system for identifying a remedial action that can be performed to address the problem. The remedial action is received and surfaced for the user.