Machine-Learning Routing for Cross-Network Virtual Resource Transfers
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Solution Overview
Problem
Conventional methods for transferring virtual resources across multiple networks face inefficiencies due to static routing protocols, sequential transfers, increased vulnerability to attacks, and delays, which are exacerbated by intermediary steps and network complexities.
Innovation Solution
A system utilizing machine learning to identify a preferred route that includes proxy nodes in a third-party network, dynamically analyzing resource requirements and network conditions to optimize transfers, enabling direct transfers from a source to a destination node by crediting and debiting the nodes upfront.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential transfers are used along an identified path, then resource transfer can be completed through intermediate nodes, but transfer time increases and bottlenecks occur
Solution Approach 1:
The system performs preliminary actions by crediting the destination node and debiting the source node before executing intermediate transfers. This upfront allocation ensures resource availability at the destination while maintaining audit trails, allowing parallel processing of transfers rather than sequential execution.
2Reliability
If sequential transfers through intermediate nodes are used, then resource routing can follow a determined path, but vulnerability to attacks and points of failure increase
Solution Approach 1:
The system extracts the critical transfer authorization function from the intermediate transfer path by crediting the destination node upfront. This separates the resource allocation decision from the physical transfer execution, reducing the security surface area exposed during intermediary transfers.
3Ease of operation
If static routing protocols are used, then routing implementation is simplified, but adaptability to dynamic network conditions and resource requirements decreases
Solution Approach 1:
The system implements dynamic routing by using machine learning models that continuously analyze network conditions, resource requirements, and transfer priorities. The routing paths are adjusted in real-time based on changing conditions, while the underlying protocol structure remains standardized for ease of implementation.
4Adaptability or versatility
If intermediate transfers are conducted, then resource routing through multiple networks is enabled, but transfer efficiency decreases
Solution Approach 1:
The system maintains continuous useful action by enabling parallel crediting and debiting operations that are not blocked by intermediate transfer completions. The resource allocation process continues uninterrupted while physical transfers execute, maximizing productivity across multi-network paths.
Data Source
AI summary
Methods and descriptions are described herein for determining preferred routes for transferring virtual resources between nodes across networks. In particular, a source node may be attempting to transfer resources to a destination node, where both nodes belong to a first network. The system determines an association between the destination node and a second network of nodes, wherein the second network includes proxy nodes associated with the first network. The system uses a machine learning model to identify a route along one or more of the proxy nodes for transfer of the resources from the source node to the destination node along the route. The system conducts the transfer of the resources by crediting the destination node and debiting the source node before conducting any intermediate transfers on the route. The system then conducts intermediate transfers between the proxy nodes along the route.