Application Performance Scoring in Information Handling Systems

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Solution Overview

Problem

Existing Information Handling Systems (IHS) face challenges in optimizing application performance, as manual user selection of optimization profiles can lead to undesirable customer experiences and inefficient resource utilization, particularly when different applications require varying levels of optimization over time.

Innovation Solution

An IHS method that assigns scores to applications based on user presence, application state, power adapter state, and hardware utilization, prioritizing performance optimization and adjusting data collection parameters accordingly to dynamically optimize application performance without user intervention.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual user selection of optimization profiles is used, then user control over application optimization is improved, but customer experience and optimization efficiency deteriorate due to complexity and user error

Engineering Contradiction:

ImproveUser controlVSAvoidOptimization efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system automatically detects running applications and assigns optimization profiles without requiring user intervention. The optimization engine monitors application execution states and autonomously selects appropriate profiles, allowing the system to serve itself rather than relying on manual user configuration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of user selection and profile assignment is replaced with an automated electronic detection and assignment system. The optimization engine uses software-based monitoring to detect applications and automatically applies profiles through system commands, eliminating the need for manual user interaction.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If optimization is based solely on resource utilization metrics, then system resource management is improved, but application performance optimization deteriorates because it doesn't account for user context and application importance

Engineering Contradiction:

ImproveResource managementVSAvoidPerformance optimization

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system changes the parameters used for optimization decisions from solely resource utilization metrics to a multi-factor approach including application type, user presence state, foreground/background status, and hardware utilization. This parameter expansion allows the system to make more reliable optimization decisions that consider both resource management and user context.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system continuously monitors multiple parameters including user presence detection, application execution state, and hardware utilization, using this feedback to dynamically adjust optimization profiles. This multi-parameter feedback loop ensures that optimization decisions are based on comprehensive system state information rather than single metrics.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If data collection frequency is maintained at high levels for all applications, then optimization accuracy is improved, but system energy consumption and processing overhead increase

Engineering Contradiction:

ImproveOptimization accuracyVSAvoidEnergy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies different data collection frequencies to different applications based on their priority and characteristics. High-priority applications in the foreground receive frequent monitoring for accurate optimization, while background applications receive reduced monitoring frequency, creating localized quality adjustments throughout the system.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system performs partial data collection for background applications rather than continuous monitoring. By collecting data at reduced intervals or only when triggered by specific events, the system maintains sufficient optimization accuracy for background tasks while significantly reducing energy consumption and processing overhead compared to continuous monitoring of all applications.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11579906B2Managing performance optimization of applications in an information handling system (IHS)

Publication Date: 2023.02.14 DELL PROD LP

AI summary

Embodiments of systems and methods for managing performance optimization of applications executed by an Information Handling System (IHS) are described. In an illustrative, non-limiting embodiment, a method may include: identifying, by an IHS, a first application; assigning a first score to the first application based upon: (i) a user's presence state, (ii) a foreground or background application state, (iii) a power adaptor state, and (iv) a hardware utilization state, detected during execution of the first application; identifying, by the IHS, a second application; assigning a second score to the second application based upon: (i) another user's presence state, (ii) another foreground or background application state, (iii) another power adaptor state, and (iv) another hardware utilization state, detected during execution of the second application; and prioritizing performance optimization of the first application over the second application in response to the first score being greater than the second score.