Server Failure Time Prediction Using Logs and Time-Series Data
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Solution Overview
Problem
Existing frameworks fail to predict server failures before they occur, leading to manual identification and resulting in business losses and unnecessary maintenance, with conventional methods producing false positives due to the complexity of server logs.
Innovation Solution
An anomaly time prediction system utilizing a combination of machine learning models, including a density auto encoder, sequential auto encoder, stochastic recurrent neural network, and time-series network, to process server logs and time-series data, enabling proactive detection of server failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of server failures is used, then false positives are reduced, but productivity decreases and business losses occur due to delayed detection
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated machine learning system that processes server logs and time-series data. The system uses trained models (LSTM, GRU, Transformer) to automatically detect anomalies and predict failures, eliminating the need for human operators to manually analyze logs while maintaining high accuracy and enabling real-time detection.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between raw server data and failure detection. These models process and interpret complex server logs and time-series metrics, transforming unstructured data into actionable failure predictions. The intermediary system bridges the gap between raw data and human decision-making, providing both automated detection and explainable insights.
2Productivity
If conventional statistical methodologies are used for server failure prediction, then processing speed is improved, but measurement precision deteriorates due to false positives from log complexity
Solution Approach 1:
The patent replaces conventional statistical methodologies with deep learning-based machine learning models. These models (LSTM, GRU, Transformer) are specifically designed to handle the complexity and temporal dependencies in server logs, providing both high processing speed through automated feature extraction and high precision through learned patterns that capture log complexity better than statistical methods.
Solution Approach 2:
The patent transforms the approach by changing from static statistical parameters to dynamic temporal parameters. The machine learning models analyze sequences of log entries over time, capturing evolving patterns and dependencies that static statistical methods miss. This parameter transformation enables the system to maintain high processing speed while achieving superior prediction accuracy.
3Device complexity
If existing frameworks are used, then device complexity is reduced, but reliability deteriorates because server failure time cannot be detected before occurrence
Solution Approach 1:
The patent implements preliminary action by training machine learning models on historical server data before deployment. The models learn normal operation patterns and failure precursors in advance, enabling them to detect early signs of impending failures. This preliminary training phase equips the system with the knowledge needed to predict failures before they occur, transforming reactive detection into proactive prediction.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors server data, compares predictions with actual outcomes, and uses this information to improve future predictions. The feedback loop allows the machine learning models to adapt to changing server behaviors and refine their failure detection accuracy over time, enhancing reliability while maintaining manageable system complexity.
Data Source
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AI summary
Methods and systems for predicting time of occurrence of future server failures using server logs and a stream of numeric time-series data occurred with a particular time window. Method performed by processor includes accessing plurality of server logs and stream of numeric time-series data, applying density and sequential machine learning model over plurality of server logs for obtaining first and second outputs, respectively, applying a stochastic recurrent neural network model over the stream of time-series data to obtain third output. The method includes aggregating first, second, and third outputs using an ensemble model, predicting likelihood of at least one future server anomaly based on the aggregating step, and determining time of occurrence of the at least one future server anomaly by capturing server behavior characteristics using time-series network model. The server behavior characteristics include time-series patterns of the stream of numeric time-series data.