Link Generation Job Segmentation for Data Packet Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Determining optimal links between data packets corresponding with execution loads and execution components is complex and requires significant computing resources, becoming impractical for situations with more than a few execution loads and components due to the NP-completeness of the calculation.
Innovation Solution
A method that calculates an overall metric for a large execution load/execution component link generation job, splits it into smaller jobs, and solves each smaller job to generate links, with the difference between job metrics and the overall metric being less than a threshold, allowing for efficient communication of links to corresponding execution components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible solutions are considered to find an optimum solution, then solution quality is improved, but computing resources and time increase exponentially
Solution Approach 1:
The patent divides the large execution load/execution component link generation job into multiple smaller sub-jobs. Each sub-job processes a subset of execution loads and components, generating links independently. This segmentation reduces the computational complexity from exponential to manageable polynomial time while producing near-optimum solutions that satisfy the overall metric threshold requirement.
2Adaptability or versatility
If the number of execution loads and execution components increases, then system capability is improved, but the complexity of finding optimal links increases exponentially
Solution Approach 1:
The patent segments the large-scale link generation problem into smaller sub-problems by dividing execution loads and components into groups. Each sub-job handles a manageable subset, allowing the system to scale to larger numbers of execution loads and components without exponential complexity growth. The segmented approach maintains adaptability while controlling computational complexity.
Solution Approach 2:
The patent accepts near-optimum solutions where the metric difference from the true optimum is within a threshold, rather than requiring complete optimization. This partial action approach (satisficing rather than optimizing) enables handling large numbers of execution loads and components with polynomial time complexity while achieving sufficiently good results for practical purposes.
3Speed
If a metric threshold is set to allow near-optimum solutions, then processing speed is improved, but solution precision decreases
Solution Approach 1:
The patent introduces a metric threshold parameter that controls the acceptable deviation from the optimum solution. By adjusting this parameter, the system can balance between processing speed and solution precision. A larger threshold allows faster processing with near-optimum results, while a smaller threshold approaches true optimum at increased computational cost. This parameter change enables flexible trade-off management.
Data Source
AI summary
Methods and systems for performing the methods are disclosed. The methods include splitting a large execution load/execution component link generation job, into a number of smaller execution load/execution component link generation jobs, and conditionally subsplitting one or more of the smaller execution load/execution component link generation jobs. The methods also include solving each of the smaller execution load/execution component link generation jobs to generate links between data packets corresponding with execution loads of each smaller execution load/execution component link generation job and data packets corresponding with execution components of each smaller execution load/execution component link generation job.