Visualizing Correlated Data Distributions for Cloud Performance Tuning
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Solution Overview
Problem
In cloud computing environments, overcommitting shared resources like memory can degrade system performance, and existing methods lack an accurate representation of throughput and response time to aid decision-making in adjusting system settings for performance tuning.
Innovation Solution
A method and system for visualizing distributions of correlated data, including throughput and response time, by collecting data from computing machines and creating a visual representation using data plots with unique visual indicators for different classes of performance information, allowing system operators to make informed adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If memory is overcommitted to increase resource utilization, then resource utilization improves, but system performance degrades
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring system performance metrics (throughput, response time, memory utilization) and using this information to dynamically adjust memory swap settings. The visual representation displays current performance status and trends, enabling operators to make informed decisions about when to adjust swappiness values to prevent performance degradation while maintaining high resource utilization.
Solution Approach 2:
The patent changes the parameter of memory swap settings (swappiness) based on observed system performance conditions. By monitoring throughput and response time metrics, the system dynamically adjusts the swappiness parameter to optimize the balance between memory utilization and system performance, transitioning from static to adaptive parameter management.
2Measurement precision
If detailed performance data is collected to improve decision-making accuracy, then decision accuracy improves, but data complexity increases
Solution Approach 1:
The patent segments detailed performance data into distinct visual categories using color-coded indicators. Different performance metrics (throughput, response time, memory utilization) are segmented and displayed separately with unique visual representations, making complex data easier to interpret while maintaining measurement precision. Each data point is segmented into manageable visual components.
Solution Approach 2:
The patent uses color changes to represent different classes of performance data. Visual indicators change color based on performance thresholds and trends, allowing operators to quickly understand complex performance states without analyzing raw numbers. This transforms complex numerical data into intuitive visual information while preserving measurement accuracy.
Data Source
AI summary
A method is described for visualizing distributions of correlated data in a computing environment in a form readable by a computer system operator, such as throughput and response time. Data is collected and a visualized representation is generated that is indicative of system performance.