Log Data Clustering for Real-Time Service Regression Detection
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Solution Overview
Problem
The existing methods for updating software systems are inefficient and time-consuming, as they rely on human programmers to detect and rectify bugs and errors post-deployment, leading to unforeseen issues.
Innovation Solution
A system for continuous delivery and service regression detection in real-time using log data clustering and machine learning techniques to generate a signature for predicting future behavior, enabling quick detection of failures through incremental and streaming-based anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human programmers manually detect and rectify bugs post-deployment, then software can be updated throughout its lifetime, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system enables self-service by implementing automated anomaly detection that monitors log data and service behavior without human intervention. The machine learning model automatically detects service regressions and generates alerts, allowing the system to identify and report bugs autonomously rather than requiring manual programmer inspection.
Solution Approach 2:
The patent replaces the mechanical manual process of bug detection with an automated electronic system. Machine learning algorithms analyze log data and service metrics automatically, substituting human programmer effort with computational analysis that operates continuously and instantaneously.
2Reliability
If software is tested before delivery to live platform, then quality can be maintained, but unforeseen errors still arise that require time-consuming human intervention
Solution Approach 1:
The system implements continuous feedback by monitoring log data and service behavior in real-time after deployment. The machine learning model learns from actual production data and provides ongoing feedback about service health, enabling detection of unforeseen errors that escaped pre-deployment testing.
Solution Approach 2:
The patent establishes a baseline of normal service behavior before deployment using historical log data. This preliminary action creates a reference model that enables immediate detection of anomalies and service regressions as soon as they occur in production, without requiring complex post-deployment investigation.
3Ease of operation
If manual processes are used for pushing updates and detecting bugs, then implementation is simple, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs self-service by automatically monitoring service behavior and detecting anomalies without requiring manual programmer intervention. The automated anomaly detection system continuously analyzes log data and identifies service regressions, eliminating the need for manual bug hunting while maintaining operational simplicity.
Solution Approach 2:
The patent implements continuous monitoring of service log data throughout the software lifecycle. Instead of periodic manual checks, the system continuously analyzes incoming log data in real-time, ensuring uninterrupted detection of service regressions and enabling immediate response to issues.
Data Source
AI summary
The present system provides continuous delivery and service regression detection in real time based on log data. The log data is clustered based on textual and contextual similarity and can serve as an indicator for the behavior of a service or application. The clusters can be augmented with the frequency distribution of its occurrences bucketed at a temporal level. Collectively, the textual and contextual similarity clusters serve as a strong signature (e.g., learned representation) of the current service date and a strong indicator for predicting future behavior. Machine learning techniques are used to generate a signature from log data to represent the current state and predict the future behavior of the service at any instant in time.