Alert Response Tool for Automated Log Analysis
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Solution Overview
Problem
On-call engineers face challenges in quickly identifying and resolving production issues due to the time-consuming process of analyzing thousands of log messages to pinpoint the source of problems, often under pressure to minimize downtime and avoid significant financial losses.
Innovation Solution
The development of an alert-response page that provides contextual insights by assembling relevant context from historical data, identifying patterns in logs and metrics, and presenting curated information to streamline the problem-resolution process, including the use of machine learning and data analytics to automatically analyze issues and suggest playbooks for incident resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If on-call engineers manually analyze thousands of log messages to identify the source of problems, then measurement precision of the problem source is improved, but loss of time increases significantly
Solution Approach 1:
The patent replaces the manual mechanical analysis of log messages by engineers with an automated machine learning system. The ML model automatically ingests log data, identifies patterns, and pinpoints problem sources without human intervention in the analysis process, thereby maintaining measurement precision while dramatically reducing the time required.
Solution Approach 2:
The patent introduces an intermediary machine learning system that acts as a mediator between the raw log data and the engineer. This intermediary automatically processes, analyzes, and presents distilled insights about problem sources, filtering out the need for engineers to manually examine thousands of individual log messages while preserving accurate problem identification.
2Reliability
If engineers examine a large pool of information including thousands of log messages, then reliability of problem identification is improved, but productivity decreases due to time consumption
Solution Approach 1:
The patent performs preliminary analysis of log messages and data enrichment automatically before the engineer needs to review anything. The machine learning system pre-processes the data, identifies potential root causes, and prepares structured insights in advance, so when the engineer reviews the case, the heavy analytical work has already been completed, ensuring both reliability and speed.
Solution Approach 2:
The patent extracts only the most relevant and critical information from the large pool of log messages and presents it to the engineer. The machine learning system filters out noise and irrelevant data, extracting only the key patterns and potential root causes, thereby maintaining reliable problem identification while dramatically reducing the information volume the engineer must process.
3Measurement precision
If manual troubleshooting processes are used with deep knowledge requirements, then measurement precision in diagnosis is improved, but device complexity of the troubleshooting system increases
Solution Approach 1:
The patent implements a self-service troubleshooting system where the machine learning model automatically performs the diagnostic functions that previously required expert engineer knowledge. The system enriches data, identifies patterns, and pinpoints root causes autonomously without requiring engineers to possess deep specialized knowledge, thereby maintaining high diagnosis accuracy while reducing the operational complexity for users.
Data Source
AI summary
Methods, systems, and computer programs are presented to generate response information for an alert. One method includes an operation for detecting an alert based on incoming log data or metric data and for calculating information for panels to be presented on a response-alert page. Calculating the information includes calculating first performance values for a period associated with the alert, calculating second performance values for a background period where the alert condition was not present, and calculating a difference between the first performance values and the second performance values. Further, the method includes an operation for selecting, based on the difference, relevant performance values for presentation in one of the panels. The response-alert page is presented with at least one of the panels based on the selected relevant performance values.