CNN Server Failure Prediction System Automating Hyperparameter Tuning
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Solution Overview
Problem
Current server failure prediction models require significant user intervention for data pre-processing and hyper-parameter optimization, leading to inefficiencies and high calculation costs, as they are often performed empirically without end-to-end learning.
Innovation Solution
An automatic prediction system utilizing a convolution neural network (CNN)-based approach that automates data pre-processing and hyper-parameter optimization through machine learning, minimizing user intervention and optimizing both processes simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data pre-processing and hyper-parameter optimization are performed manually with user intervention, then model performance can be improved through expert knowledge, but the complexity of operation and time consumption increase significantly
Solution Approach 1:
The system enables automated machine learning where the model performs self-learning and self-optimization without manual intervention. The automated model generation module automatically preprocesses data, selects features, tunes hyper-parameters, and generates prediction models, allowing the system to serve itself rather than requiring expert users to manually optimize each component.
Solution Approach 2:
The system automatically adjusts and optimizes multiple parameters including data preprocessing configurations, feature selection criteria, hyper-parameters for machine learning models, and model architecture parameters. This automated parameter optimization replaces manual expert tuning with systematic algorithmic search and evaluation.
2Reliability
If comprehensive data pre-processing and model optimization are performed manually, then prediction model performance improves, but the time required for model generation increases
Solution Approach 1:
The system performs preliminary automated actions by pre-configuring multiple candidate models with different preprocessing pipelines and hyper-parameter settings before actual prediction is needed. The automated model generation module prepares optimized models in advance through systematic exploration of parameter spaces, so when prediction is required, the system can quickly deploy pre-optimized models rather than performing manual optimization at that moment.
Solution Approach 2:
The system maintains continuous optimization through automated feedback loops where model performance is continuously evaluated and re-optimized. The automated model generation module continuously learns from new data and adjusts parameters, maintaining optimal performance without interruption or manual intervention, thereby reducing overall time loss compared to periodic manual optimization.
3Measurement precision
If multiple classification algorithms and preprocessing techniques are automatically compared, then an optimized prediction model can be generated, but the calculation cost becomes too high
Solution Approach 1:
The system applies partial action by selectively evaluating only the most promising combinations of preprocessing techniques and classification algorithms rather than exhaustively testing all possible combinations. The automated model generation module uses intelligent search strategies to identify and evaluate a subset of high-potential configurations, achieving good performance without the prohibitive calculation cost of complete enumeration.
Solution Approach 2:
The system segments the model generation process into distinct stages: data preprocessing exploration, feature selection, model training, and evaluation. By dividing the comprehensive optimization task into manageable segments, the system can efficiently explore different aspects separately and combine results, reducing overall calculation cost compared to treating all optimizations simultaneously.
4Ease of manufacture
If step-by-step optimization of data preprocessing and classification is performed separately, then each component can be optimized individually, but the overall process is not optimized
Solution Approach 1:
The system merges data preprocessing, feature selection, and classification model training into an integrated automated optimization process. The automated model generation module simultaneously optimizes preprocessing parameters and model hyper-parameters through unified evaluation metrics, ensuring that the entire pipeline is optimized together rather than separately, thereby achieving better overall performance than sequential optimization could provide.
Data Source
AI summary
The present invention relates to an automatic prediction system for a server failure, which monitors the status of a single server connected to a network and providing web, DB and network services, and predicts and warns a server failure of a target system by using the collected status data of the single server.The automatic prediction system for a server failure comprises: a data collection module to collect status information of a server and service of a target system; a model generation and optimization module to generate a CNN-based failure prediction model by using the collected data and to optimize model parameters and hyper-parameter values; and a prediction module to perform online failure prediction by using the optimized CNN-based failure prediction model.