Dynamic Resource Profile Generation for Application Latency Reduction
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Solution Overview
Problem
Existing computing devices face inefficiencies in executing software applications due to preset profiles that are not tailored to specific usage patterns, leading to suboptimal resource utilization and performance.
Innovation Solution
A computing device gathers data on resource usage and sends it to a server or edge devices for analysis using a network traffic prioritization component, which generates or selects a custom configuration profile to modify hardware and software settings, optimizing resource allocation and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If preset profiles are used for software applications, then execution efficiency is improved for particular applications, but execution efficiency deteriorates for applications different from the preset profiles or when used in atypical manners
Solution Approach 1:
The system enables computing devices to automatically generate their own custom profiles by monitoring and analyzing their own resource usage patterns. The device self-services by collecting performance data, identifying optimization opportunities, and creating tailored configuration profiles without requiring manual intervention or pre-defined templates for each application scenario
Solution Approach 2:
The system dynamically changes hardware and software parameters based on monitored resource usage patterns. By adjusting configuration parameters such as CPU allocation, memory management, and I/O settings according to actual application behavior, the system creates optimized profiles that adapt to different applications and usage scenarios rather than relying on fixed preset profiles
2Productivity
If data is sent to server or additional computing devices for profile generation, then execution efficiency is improved through custom profiles, but network latency increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and collecting resource usage data in the background before profile generation is needed. This pre-collection of performance data allows the system to quickly generate custom profiles when required, reducing the time penalty associated with network communication for profile retrieval
Solution Approach 2:
The system uses local intermediaries (additional computing devices or edge servers) that can generate profiles locally based on received usage data, reducing the need for direct communication with remote servers. This intermediary approach shortens network latency by processing profile generation requests closer to the source device
Data Source
AI summary
In some examples, a computing device executing an application may gather data associated with a usage of multiple computing resources (e.g., CPU, GPU, storage, memory, and the like) of the computing device, generate one or more packets to carry the data, and set a priority of each of the one or more packets. The computing device may send the one or more packets to a server, to other computing devices, or both. The server or the other computing devices may send a new profile. The computing device may modify a hardware and software configuration of the computing device based at least in part on the new profile to create a modified configuration and execute the application, resulting in the application having at least one of a reduced latency or an increased throughput using the modified configuration.