Vmware, Inc.

Rakesh Misra

Aditya Gudipati

Manu Sharma

Srikanth Hariharan

Rahul Tandra

Manu Bansal

Yang Yang

Sarabjot Singh

Giridhar Subramani Jayavelu

Adnan Raja

Deepak Khurana

Duyen Riggs

Amit Surendra Singh

Amit Singh

Alexandros Anemogiannis

Tianshu Chu

Kanthi Nagaraj

Rohan Agarwal

Sandeep Chinchali

Ben Basler

Rajiv Ramanathan

To provide a low latency near RT RIC, some embodiments separate the RIC's functions into several different components that operate on different machines (e.g., execute on VMs or Pods) operating on the same host computer or different host computers. Some embodiments also provide high speed interfaces between these machines. Some or all of these interfaces operate in non-blocking, lockless manner in order to ensure that critical near RT RIC operations (e.g., datapath processes) are not delayed due to multiple requests causing one or more components to stall. In addition, each of these RIC components also has an internal architecture that is designed to operate in a non-blocking manner so that no one process of a component can block the operation of another process of the component. All of these low latency features allow the near RT RIC to serve as a high speed IO between the E2 nodes and the xApps.

Running services in SDL of a RIC

Some embodiments provide a method for performing radio access network (RAN) functions in a cloud at a medium access control (MAC) scheduler application that executes on a machine deployed on a host computer in the cloud. The method receives data, via a RAN intelligent controller (RIC), from a first RAN component. The method uses the received data to generate a MAC scheduling output. The method provides the MAC scheduling output to a second RAN component via the RIC.

Cloudified MAC scheduler

RIC and RIC framework communication

Some embodiments provide a method for performing radio access network (RAN) functions in a cloud at a user-level tracing application that executes on a machine deployed on a host computer in the cloud. The method receives data, via a RAN intelligent controller (RIC), from a RAN component. The method uses the received data to generate information related to traffic performance for at least one user. The method provides the generated information to the RIC.

Cloudified user-level tracing

A method for automated root cause analysis in mobile radio access networks, including: determining mobile radio access network data (e.g., RAN data); detecting an anomaly for a set of user sessions and/or cells from the RAN data; and classifying the detected anomalies using a set of root cause classifiers.

Intelligent anomaly detection and root cause analysis in mobile networks

A system and method for training and executing an adaptive bitrate (ABR) controller can include clustering content into content clusters based on content metadata, clustering network scenarios based on network information, normalizing input measurements, training the ABR controller for each content within at least one of a given content cluster and a given network scenario cluster by determining network information and network metadata, associated with the respective content, determining a content bitrate, determining a reward associated with the content bitrate, and training the ABR controller based on the reward.

System and method for widescale adaptive bitrate selection

Systems and methods for providing mobile network guidance to applications communicating with devices via a mobile network.

Systems and methods for mobile network guidance for over-the-top applications

A graphical user interface ('GUI') allows for the creation of custom key performance indicators ('KPIs') for real-time analysis of network telemetry events. The GUI can include options for defining variables based on event attributes. These can correspond to events that exist in input telemetry streams at a stream processor. The GUI can allow creation of a formula based on these variables. An aggregation section specifies how the output of the formula is aggregated. This can be based on group, aggregation function, and time period. A manager process causes the stream processor to apply the custom KPI definition to real-time input streams. An output KPI stream can then be routed to a destination for analysis.

Composition of custom key performance indicators for real-time analysis of network telemetry events

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Palo Alto, CA, United States of America

Rakesh Misra