Cluster Health Reporting Engine for Proactive Monitoring
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Solution Overview
Problem
IT administrators face challenges in concurrently tracking the performance and health of remotely distributed computing clusters, with failures often detected only after occurrence, leading to system downtime and inefficiencies in manual on-demand data retrieval.
Innovation Solution
A cluster health reporting engine that aggregates health data across dimensions, providing automated views and enabling proactive remedial actions through input and output interfaces, configuration commands, and remediation commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual on-demand retrieval of metric data is used to monitor computing cluster performance and health, then data accuracy is maintained, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing metric data in time-series databases before any analysis is needed. Health checks and performance metrics are proactively gathered at configured intervals, so when monitoring or analysis is required, the data is already prepared and available immediately, eliminating the need for on-demand retrieval while maintaining data accuracy.
Solution Approach 2:
The patent introduces intermediary components including agents deployed on computing clusters that collect metric data locally, time-series databases that store and manage the data, and a monitoring platform that processes and analyzes the data. This intermediary architecture enables automated, continuous monitoring without requiring manual intervention, thus reducing time consumption while maintaining measurement precision through structured data collection and storage.
2Reliability
If frequent retrieval of metric data at intervals is performed to monitor computing cluster performance, then monitoring reliability is improved, but operational efficiency and resource utilization deteriorate
Solution Approach 1:
The system implements periodic action by configuring health checks and metric data collection at optimized intervals specific to each computing cluster and metric type. This ensures monitoring reliability through regular, systematic data collection while improving operational efficiency by avoiding unnecessary frequent retrievals. The monitoring platform intelligently schedules health checks based on cluster criticality and historical performance patterns.
Solution Approach 2:
The monitoring system enables self-service through automated agent-based data collection, where computing clusters automatically report their own metric data to the monitoring platform. This eliminates the need for manual or external frequent retrieval operations, maintaining monitoring reliability through continuous automated collection while significantly improving operational efficiency by reducing human intervention and resource overhead.
3Adaptability or versatility
If multiple data storage systems are distributed across computing clusters, then data accessibility and system scalability are improved, but system complexity and difficulty of concurrent tracking deteriorate
Solution Approach 1:
The monitoring platform provides universal functionality by consolidating the ability to monitor multiple different computing clusters, data storage systems, and metric types through a single unified interface. This maintains system scalability as new clusters can be added without requiring separate monitoring systems, while reducing perceived complexity by presenting a consistent monitoring approach across diverse infrastructure components.
Solution Approach 2:
The patent introduces intermediary agents and a central monitoring platform that mediate between distributed data storage systems and administrators. These agents standardize data collection across different cluster configurations and present unified health metrics, maintaining scalability of the distributed architecture while reducing the complexity of concurrent tracking by abstracting away the heterogeneity of multiple systems through a common monitoring layer.
Data Source
AI summary
A cluster health reporting engine may be a software tool which generates compiled health data reported by data collection hosts, being health data of computing resources of backend computing clusters whose failure during the ordinary course of data query and processing functions may impede the normal functioning of those data query and processing functions. Such techniques may generate compiled health data reported by a data collection host for a particular host of a computing cluster, enabling administrative personnel to quickly narrow specificity of health data reported. Such techniques may aggregate health data reported by a data collection host over a dimension of hosted services, and may configure a reporting sub-system to visualize this aggregated health data, enabling administrative personnel to quickly view storage capacity consumed by various hosted services and identify hosted services or sub-services generating adverse health data by visual highlighting.