Virtual Resource Routing via Proxy Nodes and ML Path Selection
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Solution Overview
Problem
Conventional methods for routing 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 within a third-party network, dynamically analyzing resource requirements and network conditions to optimize transfers, allowing direct transfers from a source to a destination node after crediting and debiting the nodes involved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static routing protocols are used for transferring virtual resources, then the routing process is simple and stable, but the transfer efficiency is low and the system cannot adapt to dynamic network conditions
Solution Approach 1:
The patent implements dynamic routing by using machine learning models that continuously learn from network conditions and transfer patterns. The system adapts routing decisions in real-time based on changing network states, resource requirements, and performance metrics, transforming the static routing protocol into a dynamic system that optimizes transfer efficiency while managing complexity through automated learning.
Solution Approach 2:
The system incorporates feedback mechanisms where transfer performance data is collected and fed back to the machine learning model. This feedback loop enables the routing system to learn from past transfers and continuously improve its routing decisions, allowing the system to adapt to dynamic conditions while maintaining manageable complexity through data-driven optimization.
2Loss of time
If sequential transfers are performed along an identified path, then the transfer process is simple to manage, but the total transfer time is significantly increased due to delays at each intermediate step
Solution Approach 1:
The system performs preliminary actions by pre-processing routing paths and preparing transfer sequences using machine learning predictions. The ML model anticipates optimal routing decisions and prepares transfer sequences in advance, reducing actual transfer time by eliminating decision-making delays during the transfer process itself, while managing complexity through automated pre-computation.
Solution Approach 2:
The patent implements continuous useful action by optimizing transfer sequences to minimize idle time at intermediate nodes. The machine learning system analyzes and sequences transfers to ensure continuous progression through the network, reducing delays and maintaining constant productive action throughout the transfer process, thereby reducing total transfer time while managing operational complexity.
3Reliability
If multiple intermediary transfers are conducted along a route, then the routing flexibility is increased, but the vulnerability to attacks and points of failure is significantly increased
Solution Approach 1:
The system performs preliminary security assessments and risk evaluations using machine learning models before executing transfers. The ML system predicts potential security risks and identifies vulnerable intermediary nodes in advance, allowing the system to pre-select safer routing paths or implement protective measures before transfers begin, thereby enhancing security without sacrificing routing flexibility.
Solution Approach 2:
The patent introduces trusted intermediary nodes that are verified and monitored by the machine learning system. These specialized intermediary nodes act as secure mediators in the transfer process, providing the necessary routing flexibility while maintaining enhanced security through controlled and monitored intermediate steps. The ML system identifies and prioritizes trustworthy intermediaries to reduce overall vulnerability.
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.