Automated Statistical Graphing for Distributed System Metrics
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Solution Overview
Problem
Managing and analyzing operational data from distributed computing systems poses challenges for service providers and clients, as existing monitoring tools struggle to efficiently collect, process, and visualize performance metrics across geographically and logically separate environments.
Innovation Solution
Implementing automated statistical graphing tools that monitor operational metrics, calculate percentiles, and provide visual representations such as graphs and charts to identify anomalies and operational issues, allowing for enhanced analysis and recommendation of remediation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated statistical graphing tools are implemented to monitor and visualize operational metrics, then measurement precision and analytical capability are improved, but device complexity increases
Solution Approach 1:
The patent introduces automated statistical graphing tools as an intermediary layer between raw operational data collection and human analysis. These tools automatically calculate percentiles, generate statistical graphs, and provide visual representations of operational metrics, thereby enhancing measurement precision without requiring direct human intervention in the complex data processing pipeline.
Solution Approach 2:
The monitoring system performs self-service by automatically calculating statistical metrics and generating visual representations without requiring manual configuration or intervention. The system autonomously processes operational data, computes percentiles, and produces graphical outputs, reducing the complexity burden on users while maintaining high measurement precision.
2Loss of information
If comprehensive operational metrics are collected and analyzed across distributed systems, then information completeness is improved, but loss of time in data processing increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing statistical metrics such as percentiles and other aggregated statistics as operational data is collected. This allows the system to maintain complete operational information while enabling rapid retrieval and analysis without reprocessing raw data, thereby reducing time loss in subsequent analytical operations.
Solution Approach 2:
The data processing is segmented into distinct statistical calculations (e.g., percentile computations, trend analysis, anomaly detection) that can be performed independently and in parallel. This segmentation allows comprehensive operational metrics to be analyzed without sequential processing bottlenecks, reducing overall data processing time while maintaining information completeness.
3Ease of operation
If visual representations such as graphs and charts are generated for operational data, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The graphing tools operate autonomously by automatically selecting relevant operational metrics, calculating appropriate statistical representations, and generating visual graphs and charts without requiring manual configuration. This self-service capability enhances ease of operation for users while encapsulating the complexity within the automated tool itself rather than requiring user expertise.
Solution Approach 2:
The automated graphing tools serve as an intermediary that translates complex operational data into intuitive visual representations. These tools handle the complexity of data processing, statistical calculation, and graph generation, while presenting simplified visual outputs that improve ease of operation for end users analyzing distributed system performance.
Data Source
AI summary
Statistics of a distributed computing system are managed to identify and/or provide percentile data associated with the managed statistics. In some examples, percentiles associated with performance data of the computing system may be calculated. Based at least in part on a factor associated with the performance data, one or more of the calculated percentiles may be selected. Additionally, in some examples, graph data for at least a portion of the selected percentiles may be provided.