Service Issue Prioritization via Telemetry Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software developers face a time-consuming process in investigating and prioritizing service issues due to limited information in logs, making it difficult to determine the root cause and prioritize issues effectively.
Innovation Solution
A system that correlates reported service issues with system telemetry data to rank their importance using a statistical approach, extracting telemetry IDs from logs, computing problem signatures, and automatically prioritizing issues based on their impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If service issues are prioritized manually by developers based on log information, then prioritization can be performed with available data, but the process is time-consuming and lacks objectivity
Solution Approach 1:
The system performs preliminary actions by automatically collecting and analyzing telemetry data before developers need to prioritize issues. Telemetry identifiers are extracted from logs and correlated with system performance data in advance, so when prioritization is needed, the analytical work has already been completed, dramatically reducing the time developers spend on this task.
Solution Approach 2:
The patent introduces telemetry data as an intermediary between the service issue logs and the prioritization decision. Instead of developers directly analyzing logs and making subjective judgments, the system uses telemetry metrics (CPU usage, memory consumption, response times) as an objective mediator to quantify issue impact and determine priority automatically.
2Device complexity
If limited log information is used for prioritization, then the process can be simple, but the accuracy and objectivity of prioritization deteriorates
Solution Approach 1:
The system merges telemetry data with log information to create a comprehensive view of service issues. By combining structured telemetry metrics (CPU usage, memory consumption, response times) with unstructured log data, the system achieves both simplicity in implementation and high accuracy in prioritization, resolving the contradiction between process complexity and measurement precision.
Solution Approach 2:
The patent transforms qualitative log information into quantitative parameters by extracting telemetry identifiers and correlating them with numerical telemetry data. This parameter change from text-based logs to metric-based analysis enables objective, precise prioritization while maintaining process simplicity through automated calculation of impact scores.
3Adaptability or versatility
If manual prioritization is performed, then developer judgment can be applied, but consistency and scalability of prioritization deteriorates
Solution Approach 1:
The system implements feedback by automatically calculating prioritization scores based on telemetry data and feeding these objective metrics back into the prioritization process. This creates a consistent, repeatable mechanism where the same telemetry-based rules are applied uniformly to all issues, ensuring reliability and consistency while the system remains adaptable to different service types through configurable parameters.
Data Source
AI summary
A system is provided herein that can correlate service issues with system telemetry associated with the software session associated with those service issues. Using a statistical approach, the system can evaluate data across numerous software sessions to rank the importance of the reported service issues. To accomplish the ranking, the system can parse the reports of service issues on a periodic basis, can extract telemetry identifiers (IDs) from the logs, can query the telemetry, may compute the relative importance of detected issues (in the context of calls going on for that day), and then can report this impact hack to the service issue database.