PROVIDING HIGH AVAILABILITY IN A SOFTWARE DEFINED NETWORK

    公开(公告)号:US20200007629A1

    公开(公告)日:2020-01-02

    申请号:US16021363

    申请日:2018-06-28

    Abstract: In one example, a method for monitoring the state and health of a centralized software defined networking controller includes detecting, by a standby software defined networking controller, when a number of path computation client sessions reported by an active software defined networking controller fails to match an expected number; verifying, by the standby software defined networking controller after expiration of a predefined delay implemented after the detecting, that the number of path computation client sessions reported by the active software defined networking controller still fails to match the expected number; and assuming, by the standby software defined networking controller after the verifying, a role of the active software defined networking controller.

    Routing Stability in Hybrid Software-Defined Networking Networks

    公开(公告)号:US20190190814A1

    公开(公告)日:2019-06-20

    申请号:US15845335

    申请日:2017-12-18

    Abstract: Concepts and technologies disclosed herein are directed to routing stability in a hybrid software-defined networking (“SDN”) network in which control plane functionality is shared between a centralized SDN controller and a plurality of local routers. The controller can collect data plane messages from the plurality of local routers, extract information corresponding to source nodes and edges of a graph representative of the hybrid SDN network, and store the information as entries in a table. The controller can identify any outdated entries and remove any outdated entries from the table. The controller can obtain recovered information missing from the information collected from the data plane messages. The controller also can calculate an effective capacity of the edges. The controller can then generate a stable routing pattern based upon the recovered information and the effective capacity. The controller can deploy the stable routing pattern in the hybrid SDN network.

    MACHINE LEARNING TECHNIQUES FOR SELECTING PATHS IN MULTI-VENDOR RECONFIGURABLE OPTICAL ADD/DROP MULTIPLEXER NETWORKS

    公开(公告)号:US20200313788A1

    公开(公告)日:2020-10-01

    申请号:US16902197

    申请日:2020-06-15

    Abstract: Devices, computer-readable media and methods are disclosed for selecting paths in reconfigurable optical add/drop multiplexer (ROADM) networks using machine learning. In one example, a method includes defining a feature set for a proposed path through a wavelength division multiplexing network, wherein the proposed path traverses at least one link in the network, and wherein the at least one link connects a pair of reconfigurable optical add/drop multiplexers, predicting an optical performance of the proposed path, wherein the predicting employs a machine learning model that takes the feature set as an input and outputs a metric that quantifies predicted optical performance, and determining whether to deploy a new wavelength on the proposed path based on the predicted optical performance of the proposed path.

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