Electronic Device Performance Forecasting via Statistical Modeling
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Solution Overview
Problem
As computer systems grow larger and more complex, managing interactions between electronic devices becomes challenging due to inefficiencies and bottlenecks, making it difficult to predict and improve the performance of individual devices within the system.
Innovation Solution
A method and system for collecting and analyzing performance data from electronic devices using statistical models, such as linear and exponential models, to forecast future performance and identify potential capacity issues, allowing for targeted upgrades and adjustments to prevent performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If computer systems grow larger and more complex to handle increased workloads, then system capacity and functionality are improved, but performance management becomes more difficult due to harder-to-define interactions and bottlenecks between devices
Solution Approach 1:
The patent segments the complex system performance management into individual device-level analyses. By collecting and analyzing performance data for each electronic device separately using statistical models, the system breaks down the overwhelming complexity of managing interactions between multiple devices into manageable, device-specific performance assessments.
Solution Approach 2:
The patent introduces statistical models as intermediary tools between raw performance data and performance predictions. These models (linear, exponential, logarithmic) serve as mediators that process complex interaction data and translate it into understandable performance forecasts and capacity assessments for individual devices.
2Loss of information
If traditional performance monitoring methods are used in complex systems, then data collection is performed, but accurate prediction of future performance and identification of bottlenecks becomes difficult due to irrelevant data and complex interactions
Solution Approach 1:
The patent extracts relevant performance information from complex system data by analyzing each device independently. By focusing on individual device performance metrics and removing the complexity of inter-device interactions from the analysis, the system extracts clear performance trends and predictions that would be obscured in aggregate system-level analysis.
Solution Approach 2:
The patent changes the analytical parameters from system-level aggregate metrics to device-level specific metrics. By applying statistical models to individual device performance data rather than system-wide data, the analysis parameters are transformed to reveal device-specific trends, capacity issues, and bottlenecks that are invisible in traditional system-level monitoring.
3Measurement precision
If system-wide performance analysis is performed, then overall system trends are identified, but individual device capacity issues and bottlenecks are obscured by aggregate data
Solution Approach 1:
The patent segments performance analysis from system-wide to device-level by collecting and analyzing performance data for each electronic device independently. This segmentation preserves individual device performance information that would be lost in aggregate analysis, enabling precise identification of device-specific capacity issues and bottlenecks.
Data Source
AI summary
A performance management system and method for generating a plurality of forecasts for one or more electronic devices is presented. The forecasts are generated from stored performance data and analyzed to determine which devices are likely to experience performance degradation within a predetermined period of time. A single forecast is extracted for further analysis such that computer modeling may be performed upon the performance data to enable the user to predict when device performance will begin to degrade. In one embodiment, graphical displays are created for those devices forecasted to perform at an undesirable level such that suspect devices may be subjected to further analysis.