Abstract: A real-time radio intelligent controller (RIC) executes in parallel with one or more virtual radio access network functions to provide real-time analytics and control of the virtual radio access network functions. At least a first processor core is configured to execute a radio network virtual function. The radio network virtual function is configured with a codelet to output selected operational data to a first stream associated with a first stream ID and receive control information from a control stream associated with a second stream ID. At least a second processor core is configured to execute the real-time RIC isolated from the at least the first processor core. The real-time RIC includes one or more dynamically loaded programs configured to: access the first stream; perform processing on the operational data; and write commands for the radio network virtual function to the control stream.

Abstract: A system, method, and computer-readable media for executing applications for radio interface controller (RIC) management are disclosed. The system includes one or more far-edge datacenters including first computing resources configured to execute a radio access network (RAN) function and a real-time RIC; one or more near-edge datacenters including second computing resources configured to execute a core network function and at least one of a near-real-time RIC or a non-real-time RIC; and a central controller. The central controller is configured to: receive inputs of application requirements, hardware constraints, and a capacity of the first and the second computing resources; select, based on a policy applied to the inputs, a location a far-edge datacenter or a near-edge datacenters for executing each of a plurality of applications to form a pipeline; and deploy each of the applications to the real-time RIC, the near-real-time RIC, or the non-real-time RIC based on the selected location.

Abstract: Methods and apparatuses for improving the performance and energy efficiency of Radio Access Networks (RANs) are described. Various power control schemes may dynamically adjust RAN power consumption based on fluctuations in network traffic, throughput, latency, queue sizes, and/or packet error rates with the goal of increasing energy efficiency while maintaining quality of service metrics. The power control schemes may be implemented using a PRB controller for dynamically allocating physical resource blocks (PRBs) to user devices and a CPU controller for assigning CPU power profiles based on PRB allocations for the user devices. The PRB controller and CPU controller may periodically acquire real-time telemetry data and wireless network performance information and then adjust the number of PRBs for user devices and adjust the CPU power profiles for executing RAN functions based on the telemetry data and wireless network performance information.

Abstract: Methods and apparatuses for improving the performance and energy efficiency of Radio Access Networks (RANs) are described. Various power control schemes may dynamically adjust RAN power consumption based on fluctuations in network traffic, throughput, latency, queue sizes, and/or packet error rates with the goal of increasing energy efficiency while maintaining quality of service metrics. The power control schemes may be implemented using a PRB controller for dynamically allocating physical resource blocks (PRBs) to user devices and a CPU controller for assigning CPU power profiles based on PRB allocations for the user devices. The PRB controller and CPU controller may periodically acquire real-time telemetry data and wireless network performance information and then adjust the number of PRBs for user devices and adjust the CPU power profiles for executing RAN functions based on the telemetry data and wireless network performance information.

Abstract: The present disclosure generally relates to improving energy efficiency of reality-based headsets by enabling discontinuous transmission (DRX) without significantly reducing the quality of experience (QoE) of a user of the headsets when consuming digital content presented thereon. The present disclosure includes a predictive content management system that obtains consumable content (including predictive content) to be consumed on a wearable UE. The system receives, from a radio access network (RAN), discontinuous reception (DRX) configuration information indicating active and inactive periods with which the UE and RAN may communicate. The system additionally facilitates stacking consumable content, delivering the consumable content based on the DRX configuration information, and performing certain latency masking features to avoid loss of quality of the presented content.

Abstract: Techniques are disclosed for managing resources in a computing network comprising a plurality of computing nodes and an orchestrator. Resources are identified that are available for use by workloads to be executed in the computing network. The resources are partitioned into resource classes and exposed to the computing nodes. When a requested resource is reserved for exclusive use, the partitioning of the resources is updated.

Abstract: To meet the stringent 5G radio access network (RAN) service requirements, layers one and two need to be processed in essentially real time. Thus, prompt anomaly detection is important to prevent negative impacts on customer experience, which is critical for mobile networks to meet the stringent service requirements. However, monitoring networks for anomalies is difficult due at least to (1) the resource constrained edge deployments in which the vRAN resides, (2) the variety of anomaly types and fault locations making anomalies difficult to detect, and (3) the low frequency of anomalies leading to unbalanced data sets for training, among others. The present application addresses these issues by decoupling anomaly detection at the infrastructure layer (servers, NICs, switches, etc.) from anomaly detection at the VNF layer (L1, high-DU, CU). This enables different techniques for identifying anomalies and for reducing the monitoring overhead that is tailored to each layer.

Abstract: Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Abstract: Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Abstract: Described are examples for providing fine-grained real-time pre-emption of codelets based on a runtime threshold. A controller inserts checkpoints into extended Berkeley packet filter (eBPF) bytecode of a third-party codelet prior to verification of the third-party codelet. A device executes the codelet at a hook point of an application. The inserted checkpoints determine a runtime of the codelet. The device terminates the codelet in response to the runtime exceeding a threshold. The application can be a virtualized radio access network (vRAN) network function and the codelet can control the vRAN function or export network metrics. The application may be executed in a container management system that modifies a container for the application to mount code including a function associated with the hook point of the application to the container; detect an annotation for the container that identifies the codelet; and symbolically links the codelet to the hook point.

Abstract: Described are examples for monitoring performance metrics of one or more workloads in a cloud-computing environment and reallocating compute resources based on the monitoring. Reallocating compute resources can include migrating workloads among nodes or other resources in the cloud-computing environment, reallocating hardware accelerator resources, adjusting transmit power for virtual radio access network (vRAN) workloads, and/or the like.

Abstract: Described are examples for receiving, from one or more second virtual radio access network (vRAN) workloads operating one or more second cells, an indication of a measurement of at least a first signal transmitted by a first vRAN workload operating a first cell, computing, based on measurements of at least the first signal as received from the one or more second vRAN workloads, a boundary of the first cell, and adjusting, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.

Abstract: The systems and methods relate to virtual radio access networks (vRANs). The systems and methods may offload a signal processing task of a physical layer from a vRAN server located at the far edge of a network nearby a base station to a remote location further away from the base station. The remote location may include higher level edge deployments of servers or a cloud deployment of servers. The system and methods may scale the vRAN server capacity by offloading the signal processing task to the remote location without compromising quality of service requirements or latency requirements of the user equipment or the applications.

Abstract: Described are examples for using codelets executing within applications to use machine-learning (ML) models to infer a result based on application data. The codelets may be dynamically loaded into the applications during execution. A controller verifies, based on extended Berkeley packet filter (eBPF) bytecode of the codelet, that the codelet satisfies safety requirements for execution within the application. A computing device executing the application loads the verified codelet into a library of the application. The application executes the verified codelet to apply application data to the machine-learning model to infer a result. The ML model may be implemented by the eBPF code of the codelet or the codelet may include a call to a machine-learning model of a type supported by a controller of the application and a map for a serial representation of the machine-learning model. The computing device may reconstruct the ML model based on the serial representation.

Abstract: Described are examples for providing fine-grained real-time pre-emption of codelets based on a runtime threshold. A controller inserts checkpoints into extended Berkeley packet filter (eBPF) bytecode of a third-party codelet prior to verification of the third-party codelet. A device executes the codelet at a hook point of an application. The inserted checkpoints determine a runtime of the codelet. The device terminates the codelet in response to the runtime exceeding a threshold. The application can be a virtualized radio access network (vRAN) network function and the codelet can control the vRAN function or export network metrics. The application may be executed in a container management system that modifies a container for the application to mount code including a function associated with the hook point of the application to the container; detect an annotation for the container that identifies the codelet; and symbolically links the codelet to the hook point.

Abstract: The systems and methods relate to virtual radio access networks (vRANs). The systems and methods may offload a signal processing task of a physical layer from a vRAN server located at the far edge of a network nearby a base station to a remote location further away from the base station. The remote location may include higher level edge deployments of servers or a cloud deployment of servers. The system and methods may scale the vRAN server capacity by offloading the signal processing task to the remote location without compromising quality of service requirements or latency requirements of the user equipment or the applications.

Abstract: A reliable network function virtualization (rVNF) system includes a virtualized network function (VNF) application instance that includes a plurality of physical VNF instances. A load balancer provides an interface between a client and the VNF application instance. A load balancer interface facilitates delivery of packets related to a particular user context to the same physical VNF instance. A communication interface facilitates communication between the client and the VNF application instance. Application storage stores session data associated with the VNF application instance.

Abstract: Described are examples for using codelets executing within applications to use machine-learning (ML) models to infer a result based on application data. The codelets may be dynamically loaded into the applications during execution. A controller verifies, based on extended Berkeley packet filter (eBPF) bytecode of the codelet, that the codelet satisfies safety requirements for execution within the application. A computing device executing the application loads the verified codelet into a library of the application. The application executes the verified codelet to apply application data to the machine-learning model to infer a result. The ML model may be implemented by the eBPF code of the codelet or the codelet may include a call to a machine-learning model of a type supported by a controller of the application and a map for a serial representation of the machine-learning model. The computing device may reconstruct the ML model based on the serial representation.

Abstract: A method for adjusting discontinuous reception (DRX) behavior of a user equipment (UE) to conserve energy use includes exposing a DRX application programming interface (API) that enables DRX parameters to be changed and defining a conflict resolution policy that controls when requests to change the DRX parameters should be granted. The method also includes receiving, via the DRX API, a request from an application to change a DRX parameter for the UE. The UE is in wireless communication with a base station, and the application is configured to send data to the UE via a mobile network that comprises the base station. The method also includes determining, based at least in part on the conflict resolution policy, that the request should be granted and sending a command to the base station that causes the base station to communicate a new value of the DRX parameter to the UE.

Abstract: A reliable network function virtualization (rVNF) system includes a virtualized network function (VNF) application instance that includes a plurality of physical VNF instances. A load balancer provides an interface between a client and the VNF application instance. A load balancer interface facilitates delivery of packets related to a particular user context to the same physical VNF instance. A communication interface facilitates communication between the client and the VNF application instance. Application storage stores session data associated with the VNF application instance.