Machine-learning optimization for computing networks
OwnerWesley Howard Jensen, Osamah Mohammed Aliinventors
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Abstract
A machine-learning optimization of a plurality of networks is provided. The machine-learning optimization includes interconnecting an online platform providing a machine learning module, a core network of computers deploying novel software, and a plurality of Internet network service providers. The platform collects, via the software, performance data of the Internet networks, which the machine learning module utilizes to enhance performance and reduce the latency therein networks by taking into account thousands of real-time and historic latency and bandwidth metrics. Thereby the software continually selects an optimal path through the plurality of Internet networks.
Claims7
A computer-implemented method of optimizing data flow through a plurality of networks, comprising:
- providing an online platform providing a machine learning module;
- interconnecting the online platform and a plurality of network providers via at least one computer having software for causing the at least one computer to perform the following process steps:
- (a) acquiring a list of open shortest path first (OSPF) neighbors of the plurality of network providers;
- (b) acquiring a list of label distribution paths (LDP) neighbors;
- (c) executing for each OSPF neighbor a bandwidth test for collecting latency information thereof;
- (d) prompting the machine learning module to predict multiple path outcomes through the plurality of networks and calculate a confidence score of each path outcome based on latency information for associated OSPF neighbors;
- and
- (e) making routing decisions for each OSPF neighbor based on its respective confidence score.
Dependencies are inferred from the claim text.