Kneepoint Analysis for Computer System Performance Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer system performance monitoring and prediction methods lack efficiency in identifying non-linear performance responses and scaling issues, particularly in determining whether system performance will scale exponentially or faster with increased load.
Innovation Solution
The method involves characterizing throughput using a queuing network model, collecting runtime performance data, defining an operating curve, identifying a 'kneepoint' through kneepoint analysis, and setting utilization bounds to predict performance scaling by comparing given utilization values to the kneepoint coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional performance monitoring methods are used, then system performance can be tracked, but the ability to identify non-linear performance responses and predict scaling issues is insufficient
Solution Approach 1:
The patent extracts the critical performance characteristic by identifying the kneepoint on the operating curve, which represents the threshold where non-linear performance degradation begins. This extraction transforms complex performance data into a single actionable metric (the kneepoint coordinate) that can be easily monitored and compared, resolving the contradiction between measurement precision and analysis complexity
Solution Approach 2:
The patent changes the parameter of performance analysis from monitoring multiple raw metrics to monitoring the derived kneepoint parameter. By transforming the operating curve data into a kneepoint coordinate (utilization threshold), the system achieves precise identification of non-linear performance responses while simplifying the analysis to a single parameter comparison
2Reliability
If detailed operating curve analysis is performed to identify performance thresholds, then prediction accuracy improves, but the complexity of continuous monitoring increases
Solution Approach 1:
The patent performs preliminary action by pre-calculating the kneepoint from the operating curve during system characterization. This kneepoint value is then stored and used as a reference threshold for continuous monitoring, allowing the system to maintain high prediction reliability without the complexity of performing full curve analysis in real-time
Solution Approach 2:
The patent creates a simplified copy of the performance threshold information by extracting the kneepoint coordinate from the complete operating curve. This copied threshold value can be easily stored, transmitted, and compared during monitoring operations, maintaining prediction reliability while reducing the complexity of continuous analysis
3Ease of operation
If traditional utilization thresholds are used for alerting, then implementation is simple, but the ability to detect non-linear performance degradation is limited
Solution Approach 1:
The system performs self-service by automatically characterizing the operating curve and calculating the kneepoint threshold without requiring manual configuration. The kneepoint is derived directly from system performance data, enabling the monitoring system to adapt to each specific system's characteristics while maintaining ease of operation through automated threshold determination
Solution Approach 2:
The patent changes the alerting parameter from fixed traditional thresholds to dynamic kneepoint-based thresholds. By using the kneepoint utilization value (which captures the non-linear performance characteristics) as the alerting threshold, the system maintains simplicity in implementation while dramatically improving the precision of bottleneck detection
Data Source
AI summary
The present invention provides methods, systems, apparatus, and computer software/program code products adapted for operating in, or in conjunction with, an otherwise conventional computing system, and which enable evaluating, monitoring and predicting the performance of computer systems and individual elements or groups of elements within such computer systems.