Network Resource Allocation via Priority Ranking and Dynamic Pricing
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Solution Overview
Problem
Networked systems face challenges in efficiently allocating limited resources, such as computer resources and event tickets, due to the variability in resource quality and performance, and existing methods do not effectively prioritize resource allocation based on value or user preferences.
Innovation Solution
A resource allocation system that receives requests from distributed computer systems, ranks them based on associated criteria, and allocates resources accordingly, allowing users to select resources within designated time windows, with options for reallocation and dynamic pricing adjustments based on central tendencies of bids or offers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional resource allocation algorithms (round robin, first-come-first serve) are used, then resource allocation is simple and fair, but resource allocation efficiency and value-based prioritization are poor
Solution Approach 1:
The system performs preliminary ranking of resource requests based on associated criteria before allocation occurs. This preliminary action allows the system to prioritize high-value requests and allocate resources more efficiently, rather than using simple sequential methods.
Solution Approach 2:
The allocation system dynamically adjusts pricing and allocation based on central tendencies of bids or offers, market conditions, and resource demand. This dynamic approach improves resource allocation efficiency by responding to changing conditions rather than following fixed algorithms.
2Reliability
If dynamic pricing adjustments based on central tendencies are implemented, then resource allocation reflects market values, but system complexity and computational requirements increase
Solution Approach 1:
The system uses feedback from bids and offers to adjust pricing dynamically. By calculating central tendencies of these bids and offers, the system continuously adapts prices to reflect current market values and resource demand, ensuring fair allocation while maintaining manageable complexity through iterative adjustment.
Solution Approach 2:
The system changes pricing parameters based on central tendencies of bids and offers, market conditions, and resource availability. This parameter adjustment mechanism allows the system to reflect market values in allocations while using mathematical statistics to manage the complexity of multiple variables.
3Speed
If resources are allocated immediately when bid equals market price, then allocation speed is fast, but allocation accuracy and value optimization are reduced
Solution Approach 1:
The system performs preliminary ranking of requests based on associated criteria before final allocation. This preliminary action allows the system to identify high-value requests and allocate resources more accurately, rather than relying solely on immediate price-matching speed.
Data Source
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AI summary
Methods and systems for allocating resources, such as computer controlled resources, in a networked system are described. In an example embodiment, a plurality of requests to select one or more resources are received from distributed computer systems over a network at a receiving computer system. The receiving computer system allocates resource selection time windows to at least a portion of the requests based on one or more allocation criteria, and transmits information regarding the allocated resource selection time windows to corresponding requesting computer systems.