Data Packet Allocation Links for Execution Loads
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Solution Overview
Problem
Determining optimal links between data packets corresponding with execution loads and execution components is complex and computationally expensive, becoming impractical for systems with more than a few loads and components due to the NP-completeness of the problem, requiring excessive computing resources.
Innovation Solution
A method and system that select data packets for allocation based on availability, location, and capability attributes, generating near-optimal links using a practical amount of computing resources by iteratively linking data packets corresponding with execution loads to eligible data packets corresponding with execution components, optimizing the sequence of execution loads for each component.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible solutions are considered to find the optimum allocation, then the quality of allocation is improved, but the computational resources and time required increase exponentially
Solution Approach 1:
The patent segments the allocation problem into two distinct phases: (1) generating an initial feasible allocation using a simplified greedy approach, and (2) performing localized optimizations on this initial solution. This segmentation avoids the need to evaluate all possible solutions while still achieving near-optimal results with manageable computational resources.
Solution Approach 2:
The patent applies partial optimization by focusing computational efforts on specific aspects of the allocation problem rather than optimizing everything from scratch. The greedy algorithm provides a quick initial solution, and subsequent optimizations refine only the necessary portions, achieving sufficient optimality without exhaustive search.
2Measurement precision
If all possible solutions are considered to find the optimum allocation, then the quality of allocation is improved, but the time required increases making it impractical for large systems
Solution Approach 1:
The patent performs preliminary action by generating a feasible initial allocation using the greedy algorithm before applying any optimizations. This preliminary solution provides a starting point that is already functional, allowing subsequent refinement steps to focus on improving specific aspects rather than searching from scratch, thereby significantly reducing total computation time.
Solution Approach 2:
The patent applies partial optimization by focusing computational efforts on specific aspects of the allocation problem rather than optimizing everything from scratch. The greedy algorithm provides a quick initial solution, and subsequent optimizations refine only the necessary portions, achieving sufficient optimality without exhaustive search.
3Use of energy by stationary object
If a simplified allocation method is used, then the computational resources required are reduced, but the quality of allocation deteriorates
Solution Approach 1:
The patent segments the allocation problem into two distinct phases: (1) generating an initial feasible allocation using a simplified greedy approach, and (2) performing localized optimizations on this initial solution. This segmentation allows the system to benefit from both the computational efficiency of the simplified method and the quality improvement of targeted optimizations.
Solution Approach 2:
The patent incorporates feedback mechanisms where the initial greedy allocation is evaluated, and optimization steps are applied based on identified improvement opportunities. The system uses feedback from the initial solution quality to guide subsequent refinement efforts, ensuring that computational resources are directed toward achieving meaningful improvements in allocation optimality.
Data Source
AI summary
Systems and methods of allocating execution loads to execution components are disclosed. The systems and methods select one of the execution components and one of the execution loads based on selection criteria. The systems and methods then determine whether the selected execution load may be allocated to the selected execution component. If the selected execution load may be allocated to the selected execution component, the systems and methods allocate the execution load accordingly. If the selected execution load may not be allocated to the selected execution component, the systems and methods select another one of the execution components and another one of the execution loads for attempted allocation.