Verizon Patent and Licensing Inc.

Art Zaifman

Kirk Campbell

Raghuram Parvataneni

Anil Krishna Guntupalli

Ravi K Sharma

Parry Cornell Booker

Miguel Angel Carames

John Patrick Hickey, Iii

Srinivasa M Kalapala

Massimiliano Troiani

Jayashree Srinivasan Kilkovalavedu

Jason Bowers

Amanda C Lam

A device may receive user device application performance logs, radio access network (RAN) performance logs, and multi-access edge computing (MEC) compute logs, and may train a machine learning model with the user device application performance logs, the RAN performance logs, and the MEC compute logs. The device may identify application capacity requirements and MEC compute requirements associated with images of RAN coverage areas of RANs, and may generate labeled and normalized images of RAN coverage areas. The device may receive real time MEC compute data, RAN performance data, and user device application data, and may identify one of the labeled and normalized images of the RAN coverage areas that matches a RAN coverage area of a particular RAN. The device may predict an application capacity and MEC compute requirements for the particular RAN, and may perform one or more actions based on the application capacity and the MEC compute requirements.

Systems and methods for determining, monitoring, and predicting application capacity in multiple radio access networks

A system described herein may monitor, via a Central Unit ('CU') of a radio access network (“RAN”), radio frequency (“RF”) metrics associated with a User Equipment (“UE”). The monitored RF metrics may be based on communications between the UE and a Distributed Unit (“DU”) that is communicatively coupled to the CU. The system may identify a Multi-Access/Mobile Edge Computing (“MEC”) device that is communicatively coupled to the DU and that provides one or more services to the UE via the DU. The system may resource allocation parameters for the MEC based on the RF metrics between the UE and the DU. The system may instruct the MEC to implement the set of resource allocation parameters. The MEC may modify an allocation of MEC resources, allocated for providing the one or more services to the UE, based on the determined set of resource allocation parameters.

Systems and methods for edge device resource management and coordination based on radio frequency modeling

A device may receive historical network behavior data associated with a network that includes network devices and may receive historical weather data associated with a geographical location of the network. The device may receive historical action data identifying historical actions taken for the network in response to the historical weather data, and may train a correlation model, with the historical network behavior data, the historical weather data, and the historical action data, to generate a trained correlation model. The device may receive a weather event forecast associated with the geographical location, and may process the weather event forecast, with the trained correlation model, to determine an anticipated behavior of the network in response to the weather event forecast. The device may process data identifying the anticipated behavior, with the trained correlation model, to identify actions to take in response to the anticipated behavior and may perform the actions.

Systems and methods for optimizing a network based on weather events

An illustrative latency service system identifies, in response to a request by a user equipment (UE) device for performance of a distributed computing service, a latency performance level to which the UE device is subscribed. The system obtains real-time latency performance data for a distributed computing network that is to perform the distributed computing service for the UE device in response to the request, and, based on the real-time latency performance data, determines a characteristic of a geographic zone in which the UE device is located. The characteristic is associated with latency performance that the distributed computing network is capable of providing in the geographic zone. Based on the latency performance level and the characteristic of the geographic zone, the system arranges for the performance of the distributed computing service for the UE device by the distributed computing network. Corresponding methods and systems are also disclosed.

Methods and systems for orchestrating a distributed computing service based on latency performance levels

A device receives, from a first compute instance, a Remote Latency Call, that includes a target latency measure, a function, and one or more arguments for the function. The device clusters a plurality of compute instances into multiple clusters of compute instances based on a respective latency measure associated with each compute instance of the plurality of compute instances, and determines a first cluster of compute instances from the multiple clusters of compute instances that satisfies the target latency measure. The device causes a second compute instance within the first cluster of compute instances to execute the function, using the one or more arguments, and return one or more results.

Remote latency calls for latency clustered compute instances

A device receives, from a first compute instance, a Remote Latency Call, that includes a target round trip time (RTT), a function, and one or more arguments for the function. The device determines, responsive to the Remote Latency Call, a first cluster of compute instances that satisfies the target RTT. The device migrates code associated with the function to a second compute instance within the first cluster of compute instances, and passes the arguments to the second compute instance for execution of the function.

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Millburn, NJ, United States of America

Art Zaifman