Automated Performance Manager for Dynamic Device Configuration
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Solution Overview
Problem
Existing techniques for managing computing device operations, such as smartphones, face challenges in balancing various performance aspects like battery usage, memory, storage, and network communications, leading to inefficiencies and difficulties in optimizing device performance effectively.
Innovation Solution
An Automated Performance Manager (APM) system generates decision structures based on training data to dynamically modify configuration settings, focusing on attributes that provide the largest performance gains, allowing for real-time adjustments to improve device operations by reducing errors, increasing execution speed, and extending battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If configuration settings are manually adjusted to improve device performance, then effectiveness in specific situations increases, but the complexity of managing multiple performance aspects (battery, memory, storage, network) increases and balancing becomes difficult
Solution Approach 1:
The system enables automated self-service by allowing the computing system to automatically generate and apply configuration modifications without manual intervention. The system autonomously analyzes performance data, generates decision structures, and implements configuration changes to optimize device operations across multiple aspects including battery, memory, storage, and network settings.
Solution Approach 2:
The system dynamically changes configuration parameters based on analyzed performance data. By modifying multiple configuration settings simultaneously (battery usage, memory allocation, storage management, network communications), the system resolves the complexity of managing individual parameters through coordinated multi-parameter optimization.
2Productivity
If comprehensive device operation management is implemented to balance multiple performance aspects, then overall device effectiveness improves, but the difficulty of detecting and measuring performance effects increases
Solution Approach 1:
The system segments the complex performance measurement problem by creating separate decision structures for different performance aspects (battery, memory, storage, network). Each decision structure independently analyzes and measures specific performance metrics, making it easier to detect and measure individual performance effects while maintaining overall system optimization.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring device operations and using the collected performance data to refine and update decision structures. This feedback loop enables accurate measurement of performance effects by comparing actual outcomes against predicted outcomes, allowing the system to adapt and improve its measurement capabilities over time.
3Productivity
If automated systems are used to dynamically modify device operations, then real-time performance optimization improves, but the extent of automation required increases system complexity
Solution Approach 1:
The system performs preliminary actions by pre-generating decision structures based on historical performance data and device attributes. These pre-computed decision structures are stored and ready for rapid deployment, enabling real-time optimization without requiring complex runtime decision-making processes, thus reducing the effective automation complexity while maintaining real-time performance.
Data Source
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AI summary
Techniques are described for automatically and dynamically modifying ongoing operation of computing devices in device-specific manners, such as to improve ongoing performance of the computing devices by modifying configuration settings on the computing devices or on associated systems in communication with the computing devices. The techniques may include generating one or more decision structures that are each specific to a type of measured performance effect, and using the decision structure(s) to improve corresponding performance of a computing device, with the generating of the decision structure(s) including analyzing training data that associates prior measured performance effects with corresponding attributes of computing devices and of modification actions that were performed for the computing devices. Non-exclusive examples of modifying operation of a smartphone computing device include modifying configuration settings affecting use of one or more of the device's battery, memory, storage and network communications.