Abstract: A method for utilizing computing resources in a vRAN is described. A predicted resource load is determined for data traffic processing of wireless communication channels served by the vRAN using a trained neural network model. The data traffic processing comprises at least one of PHY data processing or MAC processing for a 5G RAN. Computing resources are allocated for the data traffic processing based on the predicted resource load. Wireless parameter limits are determined for the wireless communication channels that constrain utilization of the allocated computing resources using the trained neural network model, including setting one or more of a maximum number of radio resource units per timeslot or a maximum MCS index for the wireless parameter limits. The data traffic processing is performed using the wireless parameter limits to reduce load spikes that cause a violation of real-time deadlines for the data traffic processing.

Abstract: The techniques disclosed herein manage computing environments associated with radio access networks using a natural language interface. This is achieved through utilizing natural language processing to analyze user generated inputs and generate robust large language model queries. In various examples, the queries can include radio access network documentation, diagnostic data, and past interactions to provide custom context to the large language model. Accordingly, the query can cause the large language model to generate an operation sequence comprising a plurality of commands to interface with a resource management tool and control computing resources and supporting components. In this way, the present techniques can alleviate the technical burden on end users and minimize the risk of errors.

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: 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: An access control system is disclosed for controlling access to a resource. A request is received by a location attribute policy (LAP) server to access an encrypted resource. The LAP server accesses a resource policy that identifies requirements for granting access to the encrypted resource, such as a list of attributes of the requestor that are required and a dynamic attribute requirement of the requestor. The LAP server receives a cryptographic proof from the computing device that the requestor possesses the attributes and validates the proof based at least on information obtained from a trusted ledger. Once the proof is validated, the LAP server provides a shared secret associated with the dynamic attribute requirement to a decryption algorithm. The decryption algorithm uses the dynamic attribute shared secret in combination with one or more attribute shared secrets from the requestor to generate a decryption key for the encrypted resource.

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: A cloud-connected baseboard management controller (BMC) enables remote management of all management layers of edge-based host machines from the cloud without imposition of costs associated with firewalls and private data connections. A remote management agent incorporated into the BMC enables creation of a remote management-enabled bare-metal server paradigm in which remote management from the cloud is supported at the lowest hardware levels which provides for cost-effective utilization of network resources down to even a single isolated node.

Abstract: Described are examples for deploying workloads in a cloud-computing environment. In an aspect, based on a desired number of workloads of a process to be executed in a cloud-computing environment and based on one or more failure probabilities, an actual number of workloads of the process to execute in the cloud-computing environment to provide a level of service can be determined and deployed. In another aspect, a standby workload can be executed as a second instance of the process without at least a portion of the separate configuration used by the multiple workloads, and based on detecting termination of one of multiple workloads, the standby workload can be configured to execute based on the separate configuration of the separate instance of the process corresponding to the one of the multiple workloads.

Abstract: Described are examples for overriding a library used by a workload in a cloud-computing environment including initializing a container for a workload that includes an entry point that points to a binary to be executed by the container, causing the workload to load, based on initializing the container, an override library into the container before executing the binary, where the override library includes an override function having a function signature of a function provided by the library, and instructing the workload to execute the binary in the container, where the binary calls the function using the function signature causing the override function in the override library to be called in place of the function.

Abstract: Described are examples for providing a standby workload to replace an active workload, including initializing a container for the standby workload that includes an entry point that points to a binary to be executed by the container, modifying the entry point of the container to execute a script that monitors a communication channel for an instruction for executing the standby workload, and instructing the standby workload to execute at least one of the binary or a different binary to replace the active workload.

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 overriding a library used by a workload in a cloud-computing environment including initializing a container for a workload that includes an entry point that points to a binary to be executed by the container, causing the workload to load, based on initializing the container, an override library into the container before executing the binary, where the override library includes an override function having a function signature of a function provided by the library, and instructing the workload to execute the binary in the container, where the binary calls the function using the function signature causing the override function in the override library to be called in place of the function.

Abstract: A method for utilizing computing resources in a vRAN is described. A predicted resource load is determined for data traffic processing of wireless communication channels served by the vRAN using a trained neural network model. The data traffic processing comprises at least one of PHY data processing or MAC processing for a 5G RAN. Computing resources are allocated for the data traffic processing based on the predicted resource load. Wireless parameter limits are determined for the wireless communication channels that constrain utilization of the allocated computing resources using the trained neural network model, including setting one or more of a maximum number of radio resource units per timeslot or a maximum MCS index for the wireless parameter limits. The data traffic processing is performed using the wireless parameter limits to reduce load spikes that cause a violation of real-time deadlines for the data traffic processing.

Abstract: Described are examples for deploying workloads in a cloud-computing environment. In an aspect, based on a desired number of workloads of a process to be executed in a cloud-computing environment and based on one or more failure probabilities, an actual number of workloads of the process to execute in the cloud-computing environment to provide a level of service can be determined and deployed. In another aspect, a standby workload can be executed as a second instance of the process without at least a portion of the separate configuration used by the multiple workloads, and based on detecting termination of one of multiple workloads, the standby workload can be configured to execute based on the separate configuration of the separate instance of the process corresponding to the one of the multiple workloads.

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: 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 deploying workloads in a cloud-computing environment. In an aspect, based on a desired number of workloads of a process to be executed in a cloud-computing environment and based on one or more failure probabilities, an actual number of workloads of the process to execute in the cloud-computing environment to provide a level of service can be determined and deployed. In another aspect, a standby workload can be executed as a second instance of the process without at least a portion of the separate configuration used by the multiple workloads, and based on detecting termination of one of multiple workloads, the standby workload can be configured to execute based on the separate configuration of the separate instance of the process corresponding to the one of the multiple workloads.