VDI Failure Prediction Using ML Error Log Segregation
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Solution Overview
Problem
Traditional VDI system troubleshooting methods fail to identify the root cause of failures, leading to delays and performance degradation due to high false alert rates and lack of predictive capabilities, making it difficult to manage failures in heterogeneous VDI cloud environments.
Innovation Solution
A method and system using a machine learning model to predict failures by segregating error logs, generating prediction scores, and determining response actions, which trains on feature vectors and rules to mitigate failures in real-time across multiple VDI systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional real-time monitoring techniques are used to detect failures in VDI systems, then failures can be identified during execution, but the troubleshooting process does not identify root causes and provides high false alert rates, leading to delays in overcoming failures
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical error logs and system behaviors before failures occur. The model learns patterns and correlations in advance, enabling it to predict root causes and provide accurate predictions when failures actually happen, eliminating troubleshooting delays.
Solution Approach 2:
The patent replaces traditional mechanical monitoring systems with an AI-based machine learning system. Instead of relying on rule-based detection that generates false alerts, the system uses trained neural networks to analyze error logs and predict failures with high accuracy, automatically identifying root causes without human intervention.
2Reliability
If traditional monitoring methods are used to track VDI system failures, then failures are detected after they occur, but this reactive approach causes performance degradation due to the delay in system response
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on historical data before failures occur. The model continuously learns from past error patterns and system behaviors, enabling proactive prediction of failures before they impact system performance, thus maintaining productivity while improving reliability.
Solution Approach 2:
The system implements continuous feedback loops where prediction results and actual failure outcomes are fed back into the training data. This allows the machine learning model to continuously improve its accuracy by learning from real-world performance data, creating a self-enhancing system that maintains high productivity while improving reliability over time.
3Measurement precision
If real-time monitoring of VDI system failures is implemented, then failures can be detected during execution, but troubleshooting happens only after failure occurrence, resulting in high false alert rates and delayed system recovery
Solution Approach 1:
The patent replaces traditional mechanical monitoring with AI-based analysis. The machine learning model processes error logs and system metrics to predict failures with high precision, automatically identifying root causes and recommended actions, which eliminates the time loss associated with manual troubleshooting and reduces false alerts through intelligent pattern recognition.
Data Source
AI summary
The present disclosure relates to a method and system for predicting and mitigating failures in Virtual Desktop Infrastructure (VDI) systems. System logs is received from VDI systems. Error logs are segregated from the system logs. A prediction score is generated based on the error logs. A failure is predicted in VDI systems based on the prediction score and the error logs using a trained machine learning model. A response action associated with the predicted failure is determined. Training the machine learning model comprises receiving feature vectors associated with training error logs and one or more rules. Further, the training comprises determining a failure and a value based on the feature vectors and the one or more rules. Also, the training comprises determining a correlation between the one or more rules, the determined failure and the feature vectors.