Hybrid Performance Control for Application Resource Optimization
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Solution Overview
Problem
Current performance and resource optimization tools for applications, such as video conferencing, are static and fail to adapt to the unique characteristics of individual client devices and user preferences, leading to suboptimal performance and resource utilization.
Innovation Solution
A hybrid approach combining a static performance control, a cloud-based machine learning algorithm, with a dynamic performance control, a machine learning algorithm on the client device, to optimize application performance and resource usage. This approach uses initial application configurations from the static control and adjusts them based on real-time device and application health statistics, as well as user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static performance control is used, then initial application configurations are provided, but the system cannot adapt to individual client devices and user preferences
Solution Approach 1:
The performance control system is segmented into two independent components: a static performance control that provides baseline configurations and a dynamic performance control that adapts to individual devices. This segmentation allows the system to gain adaptability through the dynamic component while keeping the static component simple and manageable.
Solution Approach 2:
The patent introduces a dynamic performance control component that continuously adapts application configurations based on real-time device characteristics and user preferences. This dynamic element transforms the previously static system into one that can respond to changing conditions, thereby improving adaptability without requiring complete system redesign.
2Adaptability or versatility
If dynamic performance control is added, then adaptability to device characteristics is improved, but system complexity increases
Solution Approach 1:
By dividing the control system into static and dynamic segments with distinct responsibilities, the patent manages complexity through modular design. The static portion handles baseline configurations while the dynamic portion focuses solely on adaptation, making each component simpler than a monolithic system would require.
Solution Approach 2:
The patent introduces intermediary components including a device information manager and a configuration manager that mediate between the dynamic performance control and the application. These intermediaries simplify the overall system architecture by handling specific tasks such as collecting device information and managing configuration changes, thereby reducing the complexity burden on the core adaptive engine.
3Productivity
If comprehensive device monitoring is implemented, then performance optimization is improved, but resource consumption increases
Solution Approach 1:
The dynamic performance control implements partial monitoring by selectively tracking only the most critical device parameters and application metrics necessary for performance optimization. Rather than comprehensively monitoring all possible device states, the system focuses on key indicators that have the greatest impact on performance, thereby reducing resource consumption while maintaining optimization effectiveness.
Solution Approach 2:
The patent implements a feedback mechanism where the dynamic performance control continuously monitors device characteristics and application performance, then uses this information to adjust configurations. This closed-loop feedback system ensures that monitoring resources are used efficiently by only collecting data that directly contributes to performance optimization decisions, avoiding waste on redundant measurements.
Data Source
AI summary
The present technology pertains to a method to balance the performance of an application with the device health and the application runtime health statistics utilizing a hybrid approach. In the hybrid approach, the static performance control is a robust machine learning algorithm trained on data from many client devices and applications. The dynamic performance control is local to the client device and the application and reacts to the real-time device performance and application performance. Additionally, a user can provide their preferences for the performance and resource optimization of the application.