Modular Entity Task Allocation via Performance Scoring
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Solution Overview
Problem
Modular computing systems face challenges in determining the most efficient tasks for their components, as existing methods lack a systematic approach to allocate tasks based on performance scores and resource utilization, leading to suboptimal task allocation and resource management.
Innovation Solution
A computer-implemented method that obtains data on tasks performed by modular entities, determines performance scores for each task, creates entity profiles based on these scores, and allocates tasks to entities based on their profiles, using machine learning techniques to identify suitable tasks and resource allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If task allocation is performed without systematic performance scoring, then allocation simplicity is maintained, but task allocation efficiency deteriorates
Solution Approach 1:
The system segments the task allocation process into distinct phases: performance data collection, performance score calculation, entity profile generation, and task allocation decision-making. This segmentation allows each component to be optimized independently while maintaining overall system efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-calculating performance scores and generating entity profiles for modular entities before actual task allocation occurs. This advance preparation enables faster and more efficient task allocation when tasks need to be assigned.
2Adaptability or versatility
If modular entities perform general functions using general resources, then resource versatility is improved, but task performance efficiency deteriorates
Solution Approach 1:
The system applies local quality by assigning tasks to modular entities based on their specific performance characteristics and capabilities. Instead of uniform task distribution, each entity receives tasks matched to its local qualities and proven performance, optimizing overall system efficiency while maintaining resource versatility.
3Measurement precision
If task allocation is performed without entity profiles, then system complexity is reduced, but task allocation precision deteriorates
Solution Approach 1:
The system implements feedback mechanisms where performance data from executed tasks is continuously collected and used to update performance scores and entity profiles. This feedback loop progressively improves task allocation precision as the system learns from actual performance outcomes.
Solution Approach 2:
Modular entities effectively self-profile through their performance data collection and execution history. The system automatically generates and maintains entity profiles based on observed performance, reducing manual configuration complexity while improving allocation precision.
Data Source
AI summary
Systems and methods of determining tasks to be performed by a modular entity are disclosed. For instance, data associated with one or more tasks performed by one or more first modular entities within one or more modular computing environments can be obtained. Each first modular entity includes at least one modular component. A performance score can be determined for each task performed by each first modular entity. The performance scores can provide a measure of efficiency of a performance of a task by a first modular entity. An entity profile can be determined for each first modular entity based at least in part on the determined performance scores. At least one task to be performed by at least one modular entity can be determined based at least in part on the determined entity profiles for the one or more first modular entities.