Cloud Application Context-Specific Data Collection for Troubleshooting
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Solution Overview
Problem
Existing cloud applications face inefficiencies in troubleshooting due to insufficient application-level logging, leading to delays in diagnosing issues, especially when problems are sporadic and non-reproducible, requiring live debug sessions and additional user interaction.
Innovation Solution
A system and method that automatically collects context-specific data from user devices when issues arise, presenting users with a list of issue types for selection and transmitting the collected data to a back-end server for troubleshooting, covering functionalities like monitoring, antivirus, and VPN, capturing data in real-time for various issues such as DNS resolution, system slowness, and crashes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If application-level logging is used for troubleshooting, then the troubleshooting process can be initiated, but the logging is insufficient to help the support team debug a wide range of issues, requiring additional live debug sessions
Solution Approach 1:
The system performs preliminary actions by proactively collecting and storing comprehensive device data (system logs, network configuration, application state, performance metrics) before issues occur or at the first sign of problems. This pre-collected information is organized and made readily available, eliminating the need for time-consuming live debug sessions later when support teams need to investigate issues.
2Measurement precision
If comprehensive data collection is implemented to capture all troubleshooting information, then diagnostic accuracy improves, but the complexity of the data collection system increases
Solution Approach 1:
The data collection system is segmented into multiple independent modules, each responsible for collecting specific types of information (system logs, network data, application state, performance metrics). This modular approach allows comprehensive data collection while managing complexity through clear separation of concerns, making the system easier to maintain and adapt.
Solution Approach 2:
The system implements a universal data collection framework that can adapt to collect relevant information for multiple different issue types (connectivity problems, performance issues, application crashes). This multi-functional approach consolidates what would otherwise require separate specialized collection mechanisms into a single flexible system.
3Reliability
If real-time data collection is performed when issues occur, then sporadic and non-reproducible problems can be captured, but additional user interaction and sessions are required
Solution Approach 1:
The system implements self-service by automatically detecting issues through continuous monitoring and triggering data collection without requiring user initiation. When problems are detected, the system autonomously collects relevant data, presents a simplified notification to the user, and handles the entire troubleshooting data gathering process without requiring active user participation or multiple interaction sessions.
4Productivity
If detailed context-specific data is collected for each issue type, then troubleshooting efficiency improves, but the quantity of data to be managed and transmitted increases
Solution Approach 1:
The system applies local quality by collecting different types and amounts of data based on the specific issue type detected. Rather than collecting all possible data uniformly, the system tailors data collection to the particular problem (e.g., collecting network-specific data for connectivity issues, performance metrics for speed problems), thereby improving troubleshooting efficiency while managing data volume through context-appropriate selection.
Data Source
AI summary
An enterprise application executed on a user device is configured to perform steps of providing functionality for a user device while operating in background on the user device; determining an issue with the functionality of the enterprise application; presenting a user of the user device a list of a plurality of issue types for selection thereof; receiving a selection from the user of an issue type for the issue; and collecting data from the user device based on the selected issue type. The steps can further include transmitting the collected data to a back end server for troubleshooting of the issue.