Model-Based Resource Allocation for User-Priority Application Performance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information handling systems lack the ability to differentiate between applications based on user importance and relevance, leading to inefficient resource allocation that can affect application performance and user experience.
Innovation Solution
A model-based resource allocation system that classifies applications based on their importance and relevance to a user, using a three-step method involving a prioritization model, macro resource model, and dynamic resource model to dynamically allocate system resources during runtime, leveraging machine learning algorithms trained on telemetry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If all applications are executed with equal resource allocation, then system simplicity is maintained, but application performance and user experience deteriorate
Solution Approach 1:
The patent implements dynamic resource allocation that adapts to changing application requirements and user priorities. The system continuously monitors application behavior and adjusts resource allocation in real-time, transitioning from static equal allocation to dynamic differentiated allocation based on actual needs and user importance rankings.
Solution Approach 2:
The patent applies different resource allocation strategies to different applications based on their individual characteristics and user importance. Each application receives customized resource allocation rather than uniform treatment, with priority determined by user-defined importance levels and actual resource utilization patterns.
2Productivity
If resource allocation is based on time of usage, then recent applications are prioritized, but importance to user is not accurately reflected
Solution Approach 1:
The patent incorporates user feedback through importance rankings and monitoring application resource utilization patterns. The system uses this feedback to continuously refine resource allocation decisions, adjusting priorities based on both user-defined importance and actual runtime behavior rather than relying solely on temporal usage patterns.
Solution Approach 2:
The patent performs preliminary classification of applications based on user-defined importance before runtime resource allocation. Applications are pre-categorized and assigned priority levels that guide subsequent resource distribution, allowing the system to anticipate needs rather than react solely to temporal usage patterns.
3Productivity
If macro classification is used to determine most used resource, then resource allocation efficiency is improved, but runtime adaptability may be limited
Solution Approach 1:
The patent combines macro classification with dynamic runtime monitoring to balance efficiency and adaptability. While macro classification provides a baseline for efficient resource allocation based on the most used resource type, the system continuously monitors actual runtime behavior and adjusts allocations dynamically when applications exhibit different resource patterns than predicted by macro classification alone.
Data Source
AI summary
User responsiveness on an information handling system may be improved by classifying an application based on its importance and/or relevance for an individual user with the goal of prioritizing resource allocation to improve responsiveness and performance of applications. The classification may include analyzing telemetry data to determine the most important applications for a user, such as by determining an application's importance and/or relevance to a particular user, and determine the resource utilization of that application from a macro perspective. After classification, changing characteristics of an application may be monitored and used to dynamically allocate system resources to the application during runtime. In this manner, priority on resource allocations for certain resources may be adapted to fit the user and the application, and adapt to the changing requirements and scenarios. The determination of application importance and/or relevance and subsequent adaptation of system resource allocation may be performed using a model-based algorithm.