Geographic Network Allocation for Decentralized Resource Distribution
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Solution Overview
Problem
Conventional cloud computing networks face inefficiencies in resource distribution due to reliance on static network hubs, which fail to optimize resource use as demand decentralizes, leading to increased pressure on resource management.
Innovation Solution
A system that dynamically selects network allocation sites based on geographic location, segregating resources by type and considering actual locations to maximize efficiency, using a linear programming algorithm to optimize resource distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a static network hub location is used for resource distribution, then infrastructure utilization is improved and start-up costs are reduced, but resource allocation efficiency deteriorates as demand decentralizes
Solution Approach 1:
The patent applies dynamics by transitioning from a static network hub location to a dynamic resource allocation system. The system dynamically determines optimal allocation sites based on real-time geographic distribution of resources and demand patterns, allowing the network to adapt its topology continuously rather than relying on fixed infrastructure locations.
Solution Approach 2:
The patent changes the parameter of allocation decision from static location-based to dynamic efficiency-based. Instead of allocating resources based on fixed geographic hubs, the system uses linear programming to optimize allocation based on current geographic distribution patterns, resource types, and efficiency metrics, thereby resolving the contradiction between infrastructure utilization and allocation efficiency.
2Productivity
If resources are allocated based on geographic location optimization, then resource allocation efficiency is improved, but system complexity increases due to dynamic selection processes
Solution Approach 1:
The patent replaces manual or rule-based allocation mechanisms with an automated linear programming optimization system. This substitution uses mathematical models to automatically determine optimal allocation sites based on geographic data, resource types, and efficiency constraints, reducing the complexity burden of manual decision-making while achieving higher allocation efficiency.
Solution Approach 2:
The system implements self-service by automatically determining optimal allocation sites without requiring manual intervention. The linear programming model autonomously processes geographic data, resource characteristics, and efficiency metrics to generate allocation decisions, thereby improving efficiency while managing complexity through automation rather than manual oversight.
3Device complexity
If distributed resources are treated as homogenous, then system simplicity is maintained, but allocation optimization is limited
Solution Approach 1:
The patent applies segmentation by dividing distributed resources into distinct categories or types based on their characteristics and requirements. This segmentation allows the linear programming model to differentiate allocation strategies for various resource types, thereby optimizing allocation effectiveness while maintaining manageable system complexity through structured classification rather than treating all resources uniformly.
Data Source
AI summary
Systems and methods are for increasing the efficiencies of distributed resources in a network through the selection of network allocation sites that maximize efficiency of the distributed resources. The system segregates each of these into a resource type. The system then determines based on a geographic location (as opposed to a network location), the ideal network allocation site for use in allocating the distributed resources of the given type. The network allocation site represents the geographic location that forms the epicenter of distribution of the distributed resources.