Distributed Computing Task Allocation via Resource Profiling
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Solution Overview
Problem
Existing methods for allocating computing tasks in distributed environments fail to efficiently match tasks with suitable computer resources, as they do not consider the specific suitability of resources for handling tasks and sub-tasks effectively.
Innovation Solution
A system that determines computer resource profiles, task complexity, and workload to assign sub-tasks to the most appropriate resources based on their capabilities and current workload, using databases to store and manage task and resource information for efficient allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If computing tasks are allocated to computer resources without considering suitability, then allocation speed is improved, but task processing efficiency deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing computer resource profile information, task complexity metrics, and suitability scores before actual task allocation occurs. This allows the allocation process to quickly match tasks with suitable resources without performing complex evaluations in real-time, thus maintaining both high allocation speed and processing efficiency
Solution Approach 2:
The patent introduces an intermediary matching system that acts as a mediator between computing tasks and computer resources. This intermediary evaluates task-resource suitability based on pre-stored profiles and complexity metrics, enabling efficient matching without direct complex interactions between tasks and resources, thereby resolving the contradiction between allocation speed and processing efficiency
2Device complexity
If computing tasks are allocated without considering sub-task distribution, then system complexity is reduced, but workload balance deteriorates
Solution Approach 1:
The patent applies segmentation by dividing computing tasks into sub-tasks and independently allocating them to different computer resources based on suitability evaluations. This allows the system to distribute workload more effectively across multiple resources, improving workload balance while maintaining manageable system complexity through structured task decomposition
Solution Approach 2:
The system implements dynamic workload allocation by continuously evaluating task complexity and resource suitability, then adaptively distributing sub-tasks to achieve optimal workload balance. This dynamic approach allows the system to respond to changing conditions and maintain balanced workloads without requiring overly complex static scheduling mechanisms
3Measurement precision
If detailed resource profiles and task complexity analysis are implemented, then task allocation accuracy is improved, but computational overhead increases
Solution Approach 1:
The system performs detailed resource profiling and task complexity analysis in advance, storing this information for future allocations. By pre-computing these metrics, the system achieves high allocation accuracy without repeating complex calculations for each task assignment, thus reducing the computational overhead of real-time allocation decisions
Solution Approach 2:
The patent creates simplified copies or representations of resource profiles and task characteristics that can be quickly compared and matched. These copied representations retain the essential information needed for accurate allocation while requiring far less computational resources to process than the full detailed analyses, thereby reducing computational overhead while maintaining allocation accuracy
Data Source
AI summary
In some embodiments, systems and methods provide for assigning networked computer resources to computing tasks, based on, for example, task complexity and/or computer resource workload.