Cluster Machine Data Visualization for Failure Prediction

The patent introduces an intermediary system that sits between the complex cluster data and the user, providing automated analysis, correlation, and presentation of data. This intermediary handles the complexity of processing data from hundreds of heterogeneous nodes, transforming raw machine data into actionable insights without requiring users to directly manage the underlying complexity.

The patent replaces manual monitoring and troubleshooting mechanics with automated computational systems. Instead of manually searching through log files and machine data, the system uses automated algorithms, machine learning models, and intelligent agents to detect anomalies, correlate events, and identify failure causes, substituting human mechanical analysis with computational automation.

The patent applies segmentation by dividing the monitoring and analysis function into multiple independent components or agents distributed across the cluster. Each node or group of nodes can be monitored by dedicated analysis components, allowing the system to scale to hundreds of nodes without centralizing all complexity in a single monitoring point. This modular approach enables incremental scaling while maintaining manageable complexity.

The patent creates universal monitoring and analysis components that can operate across heterogeneous node types. The system uses multi-functional agents and analysis tools that can handle diverse data formats, protocols, and node architectures, allowing the same monitoring infrastructure to scale across different node types without requiring type-specific complexity for each node class.

The patent extracts only the most relevant and actionable information from the vast amount of machine data. Instead of presenting all raw data to users, the system uses automated filtering, correlation, and prioritization to extract key metrics, anomalies, and failure indicators. This extraction process maintains information completeness for analysis purposes while presenting only essential information to users, making data review manageable.

The patent transforms data from traditional flat log file formats into multi-dimensional visual representations and structured formats. By organizing data across multiple dimensions (time, node type, error category, severity), the system enables efficient searching and review through dimensional filtering and visualization, allowing users to navigate large data sets by exploring different dimensional perspectives rather than linearly searching through unwieldy text logs.