Joint Transmission Commitment Simulation for Digital Resource Allocation
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Solution Overview
Problem
In digital resource allocation and transmission, it is challenging to accurately predict the resources required to complete commitments due to the dynamic nature of resource utilization and the overlap of multiple commitments, which can affect the total resources needed during a specific time period.
Innovation Solution
A joint simulation method is employed to determine the expected resources required for completing a commitment by considering historical allocations and existing transmission commitments, accounting for transfer costs and risk factors to ensure accurate resource allocation and fair credit distribution among co-existing commitments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional resource allocation methods are used for each commitment independently, then the allocation process is simple, but the resource estimation accuracy deteriorates due to not accounting for overlapping commitments and dynamic resource utilization
Solution Approach 1:
The patent segments the resource allocation process into distinct simulation phases: (1) accessing logged historical allocation data, (2) assigning allocations to different commitments based on matching criteria, (3) determining expected required resources for each commitment by analyzing overlapping time periods, and (4) calculating transfer costs and risk factors. This segmentation allows accurate multi-commitment analysis while maintaining manageable process complexity through structured decomposition.
Solution Approach 2:
The patent performs preliminary actions by accessing and analyzing logged historical allocation data before making new resource allocation decisions. The system pre-processes historical data to establish baseline patterns of resource utilization, which are then used to improve the accuracy of expected resource requirements for current and future commitments. This preliminary analysis enables more accurate predictions while avoiding the need for complex real-time calculations during commitment execution.
2Reliability
If multiple commitments are processed simultaneously without considering overlaps, then the processing speed is fast, but the resource allocation reliability deteriorates due to inaccurate resource requirement predictions
Solution Approach 1:
The patent implements dynamic resource allocation by simulating multiple commitments concurrently and adjusting resource requirements based on overlapping time periods. The system dynamically determines expected required resources by analyzing the temporal relationships between commitments, rather than using static pre-calculated values. This dynamic approach improves reliability by accounting for actual resource utilization patterns while managing computation time through efficient simulation algorithms.
Solution Approach 2:
The patent incorporates feedback mechanisms by using historical logged data to inform and adjust resource allocation decisions for current commitments. The system continuously refines its resource estimation by comparing simulated outcomes with actual historical performance, allowing it to improve allocation reliability over time. This feedback loop enables accurate resource prediction without requiring exhaustive real-time simulations for each commitment.
3Productivity
If transfer costs and risk factors are not considered in multi-commitment allocation, then the allocation process is efficient, but the fairness of credit distribution among commitments deteriorates
Solution Approach 1:
The patent changes key parameters by introducing transfer cost calculations and risk factor assessments into the resource allocation process. Instead of simply allocating resources based on commitment specifications, the system adjusts allocations by incorporating financial parameters (transfer costs) and uncertainty parameters (risk factors). These parameter changes enable fairer credit distribution among commitments while maintaining process efficiency through structured calculation methods that build upon historical data patterns.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a joint simulation for satisfying multiple different coexisting commitments to allocate digital resources. In one aspect, a method includes accessing logged data for prior allocations of digital resources, where the logged data includes at least one property associated with respective allocations of the digital resources. Different allocations of the digital resources included in the logged data are assigned to different ones of the multiple different commitments. For each commitment to allocate digital resources, an expected required resource amount that results in the commitment being allocated at least a specified minimum resource based on the allocations of digital resources of the logged data that were assigned to the multiple different commitments is determined.