Real-Time Performance Dashboard for IT System Monitoring
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Solution Overview
Problem
In information technology, the sheer volume of performance data from numerous sources makes it difficult to present relevant information in a timely manner, leading to inefficient troubleshooting and root-cause analysis, especially with thousands of database instances generating a high volume of metric values that can result in alerts, making it hard to identify critical issues.
Innovation Solution
A real-time performance dashboard displays time-indexed lines that graphically indicate the status of monitored systems over time, with instability indicated by waves or oscillations when performance events occur and stability indicated by a flat line when no events occur, allowing for quicker identification of problems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If performance data is collected from numerous sources to improve monitoring coverage, then the completeness of performance monitoring is improved, but the complexity of presenting relevant information increases
Solution Approach 1:
The patent segments the complex performance data from numerous sources into individual time-series metrics, each representing a specific aspect of system performance. These segmented metrics are then independently processed and evaluated against thresholds to identify performance events, making the complex data manageable and analyzable
Solution Approach 2:
The patent merges multiple segmented time-series metrics into a composite status indicator that provides an overall system status. This consolidation allows administrators to view both individual metric performance and aggregate system health, reducing the complexity of presenting information from numerous sources
2Measurement precision
If multiple time-series metrics are monitored to improve detection accuracy, then the precision of performance monitoring is improved, but the volume of data to be processed increases
Solution Approach 1:
The patent extracts only the relevant information from the multiple time-series metrics by evaluating each metric against predefined thresholds to identify performance events. This extraction process filters out unnecessary data while preserving critical performance information, reducing the effective data volume that requires detailed processing
Solution Approach 2:
The patent transforms multiple time-series metric values into discrete performance event indicators by comparing them against thresholds. This parameter change converts continuous data into categorical states (normal/abnormal), significantly reducing the complexity and volume of data requiring analysis while maintaining detection precision
3Speed
If real-time processing of performance metrics is implemented to improve response time, then the speed of problem detection is improved, but the computational resources required increase
Solution Approach 1:
The patent implements partial processing by evaluating only the necessary performance metrics against thresholds to identify performance events, rather than performing exhaustive analysis on all collected data. This selective processing approach achieves real-time detection speed while minimizing computational resource consumption by focusing only on critical evaluation tasks
Data Source
AI summary
In one embodiment, a method includes causing a real-time performance dashboard to be displayed, wherein the real-time performance dashboard comprises a time-indexed line that represents a status of at least one monitored system over time, wherein the status is determined, at least in part, as a composite of a plurality of time-series performance metrics. The method further includes receiving, for a time interval, new values of at least a portion of the time-series performance metrics. The method additionally includes, responsive to a determination that at least one performance event has occurred during the time interval, causing a portion of the time-indexed line which corresponds to the time interval to graphically indicate instability. Further, the method includes, responsive to a determination that no performance event has occurred during the time interval, causing the portion of the time-indexed line which corresponds to the time interval to graphically indicate stability.