Implementing Deterministic Traffic Delivery in a Time-Sensitive Networking System

    公开(公告)号:US20250039729A1

    公开(公告)日:2025-01-30

    申请号:US18361321

    申请日:2023-07-28

    Abstract: In one embodiment, a method includes receiving a request from an access point to transmit to a TSN data payload to a wireless TSN station, identifying resource units (RUs) in a downlink channel, each RU comprising a set of RU tones, identifying access category (AC) queues, multiplexing the RUs and AC queues to generate RU and AC queue pairs, generating timing boundaries of the pairs, wherein each timing boundary represents a combination of an average airtime of each RU and an average wait time of each AC queue for transmitting a size of the TSN data payload, iteratively validating the timing boundaries with a TSN lookahead time, and determining a first RU tone from a first RU associated with a first timing boundary less than the TSN lookahead time to transmit the TSN data payload in a first AC queue to the wireless TSN station.

    Runtime security analytics for serverless workloads

    公开(公告)号:US12277210B2

    公开(公告)日:2025-04-15

    申请号:US18375149

    申请日:2023-09-29

    Abstract: Runtime security threats are detected and analyzed for serverless functions developed for hybrid clouds or other cloud-based deployment environments. One or more serverless functions may be received and executed within a container instance executing in a controlled and monitored environment. The execution of the serverless functions is monitored, using a monitoring layer in the controlled environment to capture runtime data including container application context statistics, serverless function input and output data, and runtime parameter snapshots of the serverless functions. Execution data associated with the serverless functions may be analyzed and provided to various supervised and/or unsupervised machine-learning models configured to detect and analyze runtime security threats.

    CLASSIFICATION-BASED DATA PRIVACY AND SECURITY MANAGEMENT

    公开(公告)号:US20240056488A1

    公开(公告)日:2024-02-15

    申请号:US17886030

    申请日:2022-08-11

    CPC classification number: H04L63/205 H04L63/0478

    Abstract: Techniques are described for classification-based data security management. The classification-based data security management can include utilizing device and/or data attributes to identify security modes for communication of data stored in a source device. The security modes can be identified based on a hybrid-encryption negotiation. The attributes can include a device resource availability value, an access trust score, a data confidentiality score, a geo-coordinates value, and/or a date/time value. The security modes can include a hybrid-encryption mode. The source device can utilize the hybrid-encryption mode to transmit the data, via one or more network nodes, such as an edge node, to one or more service nodes.

    Reducing interference in CBRS networks

    公开(公告)号:US11089602B2

    公开(公告)日:2021-08-10

    申请号:US16569416

    申请日:2019-09-12

    Abstract: Systems, methods, and computer-readable media for radio resource management in a Citizens Broadband Radio Service (CBRS) network include one or more CBRS devices (CBSDs) which can obtain a group type associated with the CBSDs and their associated Radio Environment Map (REM) reports of one or more frequency channels of the CBRS network. The group type and the REM reports may be provided to a Self-Organizing Network (SON) manager of the CBRS network, where the SON manager may determine and provide to the CBSDs, one or more of a channel utilization information, transmission power, or mobility load management information for the CBSD, based on the group type and the REM reports.

    RUNTIME SECURITY ANALYTICS FOR SERVERLESS WORKLOADS

    公开(公告)号:US20240028704A1

    公开(公告)日:2024-01-25

    申请号:US18375149

    申请日:2023-09-29

    CPC classification number: G06F21/52 G06N20/00 G06F21/566 G06F2221/033

    Abstract: Runtime security threats are detected and analyzed for serverless functions developed for hybrid clouds or other cloud-based deployment environments. One or more serverless functions may be received and executed within a container instance executing in a controlled and monitored environment. The execution of the serverless functions is monitored, using a monitoring layer in the controlled environment to capture runtime data including container application context statistics, serverless function input and output data, and runtime parameter snapshots of the serverless functions. Execution data associated with the serverless functions may be analyzed and provided to various supervised and/or unsupervised machine-learning models configured to detect and analyze runtime security threats.

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