SYSTEMS AND METHODS FOR RESTRICTING NETWORK TRAFFIC BASED ON GEOGRAPHIC INFORMATION

    公开(公告)号:US20210329534A1

    公开(公告)日:2021-10-21

    申请号:US16853239

    申请日:2020-04-20

    Abstract: A system described herein may provide techniques for a geographically-based traffic handling policy. An originating device may mark traffic with geographic restriction information, such as in a header of network traffic, indicating a geographic restriction on the propagation of the traffic. The geographic restriction may indicate a geographic region in which the traffic may be forwarded, or a geographic region in which the traffic is prohibited from being forwarded. Network devices in a path between the originating device and a destination device may determine whether to drop the traffic or perform other policy-related actions based on whether such devices are inside the geographic region in which the traffic may be forwarded. In some implementations a destination device may register as an exception to such policies, based on which an originating device or a router may bypass geographically-based traffic handling policies with respect to marked traffic directed to the destination device.

    Predictive modeling of energy consumption in a cellular network

    公开(公告)号:US12192806B2

    公开(公告)日:2025-01-07

    申请号:US17531847

    申请日:2021-11-22

    Abstract: A predictive modeling approach to managing cellular network infrastructure is disclosed. In an embodiment, a method can include receiving raw data from a plurality of data sources populated while operating a cellular network. The method can then generate per-logical cell site data by normalizing the raw data based on a set of LCSs in the cellular network to generate per-LCS data. The method can then generate an example from the per-LCS data and generate a predicted energy consumption value for the given LCS by inputting the example into a predictive model (e.g., a decision tree-based model, such as an XGBoost model). From this output, the method can determine if the predicted energy consumption value is higher than an expected energy consumption value (e.g., a historical range of consumption). If so, the method can then label the given LCS as an outlier.

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