Multi-Device Processing Activity Allocation for Thermal Load Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In multi-device computing environments, some devices become overutilized while others remain underutilized, leading to hardware degradation, noise, and performance issues due to excessive heat and fan usage, which existing technologies fail to address effectively.
Innovation Solution
A system utilizing machine learning models to predict the effects of computing activities on each device, dynamically allocating tasks to mitigate overutilization by minimizing heat and noise, and optimizing resource usage across multiple networked devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing tasks are concentrated on fewer devices, then processing efficiency is improved, but hardware degradation and heat generation increase
Solution Approach 1:
The patent segments computing tasks into individual computing activities and distributes them across multiple devices in a network. Each device handles specific activities rather than one device handling all tasks, which reduces heat generation and hardware degradation on any single device while maintaining overall processing efficiency.
Solution Approach 2:
The system dynamically allocates computing activities to devices based on real-time conditions such as current workload, hardware state, and predicted heat generation. This dynamic allocation allows the system to adapt to changing conditions and optimize both processing efficiency and thermal management.
2Temperature
If computing tasks are distributed across multiple devices, then heat generation is reduced, but system complexity increases
Solution Approach 1:
Each computing device monitors its own conditions (workload, temperature, hardware state) and reports this information to the allocation system. The devices autonomously provide this self-service data, reducing the need for complex centralized monitoring infrastructure while enabling intelligent task distribution.
Solution Approach 2:
The system implements feedback loops where devices report their current state and the results of executed computing activities back to the allocation system. This feedback mechanism enables continuous optimization of task distribution without requiring overly complex manual configuration or monitoring.
3Measurement precision
If real-time monitoring of device conditions is implemented, then task allocation accuracy is improved, but computational overhead increases
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing device condition data in advance of task allocation decisions. Machine learning models are trained beforehand on historical device data to predict heat generation and performance characteristics, enabling accurate allocation decisions without requiring intensive real-time computation for every monitoring event.
Solution Approach 2:
The system monitors key device conditions at strategic intervals rather than continuously, and focuses measurement efforts on the most critical parameters affecting task allocation. This partial monitoring approach reduces computational overhead while maintaining sufficient accuracy for effective task distribution.
Data Source
AI summary
Allocating processing activities among multiple computing devices can include identifying multiple computing activities of a computer-executable process and, for each computing activity identified, estimating in real time the computing resources needed. The identifying can be in response to detecting a computer-executable instruction executed by one multiple communicatively coupled computing devices, and the computer-executable instruction can be associate with the computer-executable process. A current condition and configuration of each of the computing devices can be determined in real time. For each computing device an effect induced by executing one or more of the plurality of activities can be predicted, the predicting based each computing device's current condition and configuration and performed by a machine learning model trained using data collected from prior real-time processing of example process activities. Based on the predicting, computing activities can be allocated in real time among the computing devices.