Variational outcome-mapping model for network routing
AssigneeStripe, Inc.
InventorsJoshua ACKERMAN, Michael CRIBBIN, Andrei ZAHARIA, Niall O'HARA, Aidan FOLEY, Cody LEY-HAN
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Abstract
A method and related systems may generate and use a machine learning model that determines multiple outcome values for multiple message routes, where the model may account for unknown node values in one or more nodes during model creation. Some embodiments may then compare the multiple outcome values to select a target route for the message. Moreover, some embodiments may facilitate efficient network route determination through an adaptive weight node network by selecting different dynamic routing protocols based on critical thresholds.
Claims20
A system for efficiently determining a network route through an adaptive weight node network by selecting different dynamic routing protocols based on critical thresholds, comprising one or more non-transitory media storing program instructions that, when executed by one or more processors, causes the one or more processors to perform operations comprising:
- in connection with receiving instructions to obtain a trained model for generating network routes, generating an adaptive routing tree structure based on a set of route options corresponding with a set of adaptive weight nodes indicated to change in real-time;
- generating a set of routing tree instances of the adaptive routing tree structure for training operations by (1) retrieving a set of weight boundaries for a set of adaptive weights of the set of adaptive weight nodes and (2) determining, for the set of adaptive weight nodes, a set of sensitive thresholds by perturbing the set of adaptive weights within the set of weight boundaries, the set of sensitive thresholds representing thresholds at which changes in an adaptive weight of the set of adaptive weight nodes causes a change in routes associated with an initial routing state;
- generating a prediction model to predict outcome states by using, as training data, the set of routing tree instances and training weight states associated with the set of routing tree instances, wherein generating the prediction model comprises:
- assigning, based on the set of sensitive thresholds, a first state-based category to a first training weight state associated with a first routing tree instance and a second state-based category to a second training weight state associated with a second routing tree instance;
- and
- generating a first trained sub-model associated with the first state-based category based on the first routing tree instance and a second trained sub-model associated with the second state-based category based on the second routing tree instance;
- and
- in connection with receiving a candidate network message, obtaining real-time node weight data to determine a weight state, and providing, to the prediction model, (1) the weight state and (2) an input state associated with the candidate network message as inputs or configuration parameters to determine a target route for the candidate network message.
A method for efficiently determining a network route by selecting different dynamic routing protocols, comprising:
- generating an adaptive routing tree structure based on a set of route options corresponding with a set of adaptive weight nodes;
- generating a set of routing tree instances of the adaptive routing tree structure by (1) retrieving a set of weight boundaries for a set of adaptive weights of the set of adaptive weight nodes and (2) determining, for the set of adaptive weight nodes, a set of sensitive thresholds by perturbing the set of adaptive weights within the set of weight boundaries;
- generating a prediction model to predict outcome states by using, as training data, the set of routing tree instances and training weight states associated with the set of routing tree instances;
- and
- in connection with receiving a candidate message, determining a weight state based on obtained node weight data and determining a target route for the candidate message by providing, to the prediction model, the weight state and an input state associated with the candidate message as inputs or configuration parameters.
One or more non-transitory machine-readable media storing program instructions that, when executed by one or more processors, causes the one or more processors to perform operations comprising:
- generating an adaptive routing tree structure based on a set of route options corresponding with a set of adaptive weight nodes;
- generating a set of routing tree instances of the adaptive routing tree structure by modifying a set of adaptive weights of the set of adaptive weight nodes within a set of weight boundaries to determine a set of sensitive thresholds;
- generating a prediction model to predict outcome states by using, as training data, the set of routing tree instances and training weight states associated with the set of routing tree instances;
- determining a weight state based on obtained node weight data;
- and
- determining a target route for a candidate message by providing, to the prediction model, the weight state and an input state associated with the candidate message as inputs or configuration parameters.
Dependencies are inferred from the claim text.