TECHNIQUES FOR DEPLOYING WORKLOADS ON NODES IN A CLOUD-COMPUTING ENVIRONMENT

    公开(公告)号:US20220360624A1

    公开(公告)日:2022-11-10

    申请号:US17316314

    申请日:2021-05-10

    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.

    TECHNIQUES FOR DEPLOYING WORKLOADS ON NODES IN A CLOUD-COMPUTING ENVIRONMENT

    公开(公告)号:US20230007077A1

    公开(公告)日:2023-01-05

    申请号:US17930608

    申请日:2022-09-08

    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.

    NATURAL LANGUAGE-BASED MANAGEMENT OF COMPUTING RESOURCES EXECUTING RADIO ACCESS NETWORK WORKLOADS

    公开(公告)号:US20240419920A1

    公开(公告)日:2024-12-19

    申请号:US18335745

    申请日:2023-06-15

    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.

    WIRELESS PARAMETER LIMITS FOR PREDICTED VRAN RESOURCE LOADS

    公开(公告)号:US20230388856A1

    公开(公告)日:2023-11-30

    申请号:US17825596

    申请日:2022-05-26

    CPC classification number: H04W28/0942 H04W28/24 H04W28/0289 H04L1/0003

    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.

    FINE-GRAINED REAL-TIME PRE-EMPTION OF CODELETS BASED ON RUNTIME THRESHOLD

    公开(公告)号:US20230388393A1

    公开(公告)日:2023-11-30

    申请号:US17824662

    申请日:2022-05-25

    CPC classification number: H04L67/55 H04L67/02 G06F9/45558 G06F2009/45595

    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.

    TECHNIQUES FOR OVERRIDING LIBRARIES FOR WORKLOADS IN A CLOUD-COMPUTING ENVIRONMENT

    公开(公告)号:US20230388369A1

    公开(公告)日:2023-11-30

    申请号:US17804737

    申请日:2022-05-31

    CPC classification number: H04L67/10

    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.

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