RADIO FREQUENCY PLAN GENERATION FOR NETWORK DEPLOYMENTS

    公开(公告)号:US20230180018A1

    公开(公告)日:2023-06-08

    申请号:US17582241

    申请日:2022-01-24

    CPC classification number: H04W16/18 H04W24/02

    Abstract: Examples described herein relate to generation of radio frequency (RF) plans for network deployments. Examples described herein may receive an input RF plan with modified set of features of a network deployment area. A first machine learning (ML) model generates an intermediate RF plan indicating candidate AR locations based on the modified set of features and a first set of parameters. A second ML model determines a network optimization score for the intermediate RF plan. Based on the optimization score, the first set of parameters are optimized. The first ML model generates an output RF plan indicating optimized AP locations based on the optimized first set of parameters and the modified set of features.

    COMPARING NETWORK TOPOLOGY, STATES, AND CONFIGURATION AT DIFFERENT TIME INSTANCES

    公开(公告)号:US20250112830A1

    公开(公告)日:2025-04-03

    申请号:US18477353

    申请日:2023-09-28

    Abstract: A network management system for orchestrating a network is provided. During operation, the system generates a graph representing the network. A respective vertex corresponds to an entity in the network, and a respective edge indicates a relationship between a vertex pair. The system can determine a first and a second timestamps for a respective edge. The first timestamp indicates a time instance when a relationship indicated by the edge is established. The second timestamp indicates a time instance when the relationship is terminated. The time range between the first and second timestamps indicates an active period for the edge. The system then receives, from an interface of the system, an instruction for comparing the topology, states, and configuration of the network. The system determines the topology, states, and configurations of the network at a target time instance indicated by the instruction by traversing the active edges of the graph.

    Data based scheduling for horizontally scalable clusters

    公开(公告)号:US11301299B2

    公开(公告)日:2022-04-12

    申请号:US16174487

    申请日:2018-10-30

    Abstract: An apparatus can comprise a processor and a memory. The memory can store instructions that, when executed by the processor, cause the processor to associate a plurality of consumer containers with a data container. The plurality of consumer containers can host workloads that access a data segment hosted by the data container. The plurality of consumer containers and the data container can be scheduled on different nodes of a horizontally scalable cluster. A node of the horizontally scalable cluster that hosts the data container can be identified. The plurality of consumer containers can be scheduled to execute on the node based on the association between the plurality of consumer containers and the data container.

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