Anomaly Correlation System for Software Deployment Diagnostics
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Solution Overview
Problem
Organizations face challenges in identifying and troubleshooting online service outages and performance issues caused by software deployments, as existing methods require manual and time-consuming troubleshooting processes, often involving extensive searches through log files and metrics data.
Innovation Solution
An anomaly correlation system that automatically correlates anomalies in operational metrics with software deployments by analyzing data from log files and operational metrics to determine if a software deployment is causing issues, generating a correlation strength identifier to indicate the likelihood of a software deployment being faulty and potentially rolling it back to a previous version.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual troubleshooting is used to identify service outages, then diagnostic accuracy can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The patent replaces manual mechanical troubleshooting with an automated computer-implemented system that uses algorithms to correlate operational metrics with software deployments. The system automatically detects anomalies, retrieves relevant deployment information, calculates correlation scores, and generates diagnostic reports without human intervention, thereby eliminating the time-consuming manual process while maintaining diagnostic accuracy through systematic data analysis.
Solution Approach 2:
The system enables self-service diagnostics by automatically performing the entire troubleshooting process from anomaly detection to root cause identification. The computer system independently retrieves operational metrics, correlates them with software deployment data, determines correlation strengths, and generates diagnostic conclusions without requiring engineer intervention, thus resolving the contradiction between speed and accuracy.
2Measurement precision
If extensive data searching is performed to identify deployment causes, then diagnostic completeness is improved, but computational resource consumption increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on retrieving only the specific software deployment information that is locally relevant to the detected anomaly. Instead of performing extensive global data searches, the system retrieves deployment data from a defined time window surrounding the anomaly detection, thereby achieving diagnostic completeness for the specific issue while minimizing unnecessary computational resource consumption.
Solution Approach 2:
The system performs partial action by retrieving software deployment information within a specific time range (e.g., before and after the anomaly detection time) rather than searching all historical data. This partial retrieval approach provides sufficient information to determine correlation strength and identify root causes without the excessive computational cost of analyzing the entire data history.
3Productivity
If automated correlation analysis is implemented, then troubleshooting speed is improved, but system complexity increases
Solution Approach 1:
The patent segments the automated correlation analysis system into distinct functional modules: an anomaly detection module that identifies operational metric anomalies, a data retrieval module that fetches software deployment information, a correlation analysis module that calculates correlation scores, and a report generation module that produces diagnostic results. This segmentation enables the system to achieve high troubleshooting speed through automated processing while managing complexity by organizing functions into separate, manageable components.
Solution Approach 2:
The system achieves universality by creating a multi-functional platform that can handle various types of operational metrics (error rates, response times, throughput), multiple software deployment types, and different correlation analysis methods through a single integrated architecture. This universal design improves troubleshooting speed across diverse scenarios while avoiding the complexity of maintaining separate specialized systems for each function.
Data Source
AI summary
A machine may be configured to determine correlations between anomalies in operational metrics and software deployments. For example, the machine, based on an anomaly pertaining to the running of the service, accesses an operational metric associated with a service running on a social networking service (SNS). The machine generates, based on a log file associated with a software deployment, a deployment time series of completion percentage values indicating levels of completion of the software deployment. The machine determines that a correlation exists between the operational metric and the deployment time series of completion percentage values, and generates a correlation strength identifier based on a determination of a strength of the correlation. The correlation strength identifier indicates a likelihood that the software deployment has caused the anomaly. The machine causes a presentation of the correlation strength identifier in a user interface of a client device.