ASSIGNING UWB ANCHORS FOR CLIENT RANGING

    公开(公告)号:US20220070816A1

    公开(公告)日:2022-03-03

    申请号:US17098735

    申请日:2020-11-16

    Abstract: Presented herein are techniques for assigning Ultra-Wideband (UWB) anchors for client ranging. A location server can estimate a coarse location of a mobile device using a localization technique other than a UWB localization technique. The localization technique can involve multiple wireless access points or other radio devices. The location server can define an area around the coarse location to identify a set of candidate anchors for UWB ranging. The set of candidate anchors can be disposed within the area and include at least a subset of the radio devices. The location server can modify the set of candidate anchors to create a modified set of candidate anchors that includes only UWB-enabled devices. The location server can select a primary anchor from the modified set of candidate anchors and send a command to cause a UWB ranging procedure to be initiated between the primary anchor and the mobile device.

    ENFORCING DATA SOVEREIGNTY POLICIES IN A CLOUD ENVIRONMENT

    公开(公告)号:US20210329001A1

    公开(公告)日:2021-10-21

    申请号:US17362735

    申请日:2021-06-29

    Abstract: Systems, methods, and computer-readable media for enforcing data sovereignty policies in a cloud environment are provided. An example method can include sending, by a cloud provider, to a government entity associated with a geographic area, a request for device certificates for nodes located within the geographic area; receiving device certificates for the nodes; creating a data sovereignty policy specifying that data associated with the government entity must be stored on nodes located within the geographic area; based on the device certificates, verifying those of the nodes that comply with the data sovereignty policy; and storing the data associated with the government entity on those of the nodes verified to comply with the data sovereignty policy.

    SECURE CREATION OF APPLICATION CONTAINERS FOR FIFTH GENERATION CELLULAR NETWORK SLICES

    公开(公告)号:US20210314811A1

    公开(公告)日:2021-10-07

    申请号:US16841526

    申请日:2020-04-06

    Abstract: Systems, methods, and computer-readable media for the secure creation of application containers for 5G slices. A MEC application in a MEC layer of a 5G network can be associated with a specific network slice of the 5G network. A backhaul routing policy for the MEC application can be defined based on the association of the MEC application with the specific network slice of the 5G network. Further, a SID for the MEC application that associates the MEC application with a segment routing tunnel through a backhaul of the 5G network can be generated. A MEC layer access policy for the MEC application can be defined based on the SID for the MEC application. As follows, access to the MEC application through the 5G network can be controlled based on both the backhaul routing policy for the MEC application and the MEC layer access policy for the application.

    FUNCTION-AS-A-SERVICE (FAAS) MODEL FOR SPECIALIZED PROCESSING UNITS

    公开(公告)号:US20210287323A1

    公开(公告)日:2021-09-16

    申请号:US16817390

    申请日:2020-03-12

    Abstract: A server that includes a graphics processing unit (GPU) may receive, from a first application that is remote from the server, a first request to reserve a first number of cores of the GPU for a first amount of time. The server may also receive, from a second application that is also remote from the server, a second request to reserve a second number of cores of the GPU for a second amount of time that at least partly overlaps the first amount of time. The server may determine that the first request is associated with a higher priority than the second request and, in response, may reserve the first number of cores for the first amount of time for the first application. The server may send, to the first application, an indication that the first number of cores have been reserved as requested by the first application.

    DYNAMIC ALLOCATION AND RE-ALLOCATION OF LEARNING MODEL COMPUTING RESOURCES

    公开(公告)号:US20210286650A1

    公开(公告)日:2021-09-16

    申请号:US16818955

    申请日:2020-03-13

    Abstract: This disclosure describes techniques for improving allocation of computing resources to computation of machine learning tasks, including on massive computing systems hosting machine learning models. A method includes a computing system, based on a computational metric trend and/or a predicted computational metric of a past task model, allocating a computing resource for computing of a machine learning task by a current task model prior to runtime of the current task model; computing the machine learning task by executing a copy of the current task model; quantifying a computational metric of the copy of the current task model; determining a computational metric trend based on the computational metric; deriving a predicted computational metric of the copy of the current task model based on the computational metric; and, based on the computational metric trend, changing allocation of a computing resource for computing of the machine learning task by the current task model.

    ATTRIBUTE-BASED ENCRYPTION FOR MICROSERVICES

    公开(公告)号:US20210264047A1

    公开(公告)日:2021-08-26

    申请号:US17236936

    申请日:2021-04-21

    Abstract: The disclosed technology provides solutions that enable scalable and secure data retrieval between microservices by using microservice attributes to encrypt container based data stores. A process of the technology can include steps for: instantiating a first microservice and a second microservice in a cloud environment, wherein the first microservice is associated with a first attribute label and the second microservice is associated with a second attribute label, generating a first key based on the first attribute label and a second key based on the second attribute label, associating a first data store with the first microservice, wherein the first data store is encrypted using the first key, and associating a second data store with the second microservice, wherein the second data store is encrypted using the second key. Systems and machine readable media are also provided.

    Automated power-aware green radio resource management (RRM)

    公开(公告)号:US11019565B2

    公开(公告)日:2021-05-25

    申请号:US16548727

    申请日:2019-08-22

    Abstract: The disclosed technology relates to a process for managing power consumption of Wi-Fi access points. The solution is composed of three parts. A first part creates power profiles for each of the switches having access points within a computer network that detail how power consumption for an access point can be modified based on changing one or more elements associated with the access point. The second part creates a time-based radio constraint profile that details power requirements for an access point based on client devices connected to the access point within a pre-defined area to ensure that each client device can connect and stay connected with the access point. The third part utilizes machine learning to generate a model that identifies optimal settings for the access point in order to achieve a specific level of power consumption for the access point.

    AUTOMATED POWER-AWARE GREEN RADIO RESOURCE MANAGEMENT (RRM)

    公开(公告)号:US20210058859A1

    公开(公告)日:2021-02-25

    申请号:US16548727

    申请日:2019-08-22

    Abstract: The disclosed technology relates to a process for managing power consumption of Wi-Fi access points. The solution is composed of three parts. A first part creates power profiles for each of the switches having access points within a computer network that detail how power consumption for an access point can be modified based on changing one or more elements associated with the access point. The second part creates a time-based radio constraint profile that details power requirements for an access point based on client devices connected to the access point within a pre-defined area to ensure that each client device can connect and stay connected with the access point. The third part utilizes machine learning to generate a model that identifies optimal settings for the access point in order to achieve a specific level of power consumption for the access point.

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