Abstract: This disclosure describes techniques and mechanisms for determine a change window of least impact based on the type of activity, urgency, and preference, and highlighting risk(s) of choosing a change window. The techniques streamline and automate change window technology and provide customized and personalized change window option(s) to an administrator of a network.

Abstract: Aggregated health information for a managed network may be retrieved and processed in response to changes to the managed network topology, configuration, or software. In response to receiving notification that a change to a component of the managed network has occurred, a change audit analysis engine can retrieve performance indicator information from components along a traceroute including the component which underwent the change. The retrieved performance indicator information can be processed by a memory based neural network to predict an impact of the change on the aggregated health of the managed network. The predicted impact can be compared to network health information retrieved through an ongoing basis and issues can be determined based on a comparison of the predict impact and the retrieved health information.

Abstract: A method of orchestrating one or more radio resources among various services executing within a container. The method includes obtaining, by an orchestration engine executing on a network device, a request, from a first service of a plurality of services, for use of a physical/hardware resource that connects a container running on the network device to a network. The request from the first service has a particular priority. The plurality of services execute within the container. The method further includes determining whether to connect the first service to the network via the physical/hardware resource based on the priority and an availability status of the physical/hardware resource and establishing, at a kernel level, a connection between the first service and the physical/hardware resource based on the determining.

Abstract: In one embodiment, a supervisory service for a wireless network obtains frequency-time Doppler profile information for an endpoint node attached to a first access point in the wireless network. The supervisory service uses the frequency-time Doppler profile information for the endpoint node as input to a machine learning model. The machine learning model is trained to output an action for the endpoint node with respect to the wireless network. The supervisory service causes the action for the endpoint node with respect to the wireless network to be performed.

Abstract: A network infrastructure component determines a risk measurement associated with a wireless client device's use of a device address, and provides an advisory with respect to an address rotation strategy of the wireless client device based on the risk measurement. In some embodiments, the risk measurement is based on one or more of an exposure, by the wireless client device, of information on the wireless network that identifies the wireless client device and/or a characterization of a security of the wireless network environment in which the wireless client device operates.

Abstract: A method of orchestrating one or more radio resources among various services executing within a container. The method includes obtaining, by an orchestration engine executing on a network device, a request, from a first service of a plurality of services, for use of a physical/hardware resource that connects a container running on the network device to a network. The request from the first service has a particular priority. The plurality of services execute within the container. The method further includes determining whether to connect the first service to the network via the physical/hardware resource based on the priority and an availability status of the physical/hardware resource and establishing, at a kernel level, a connection between the first service and the physical/hardware resource based on the determining.

Abstract: Techniques for access point (AP) based location computation are disclosed. A target wireless AP, communicatively coupled to a wireless station (STA), is identified. One or more location buddy APs, relating to the target AP, are identified based on the physical locations of the location buddy APs and the target AP. It is determined that a first location buddy AP, of the one or more location buddy APs, is communicatively coupled to the STA, and in response a location of the STA is determined using the target AP.

Abstract: A method of orchestrating one or more radio resources among various services executing within a container. The method includes obtaining, by an orchestration engine executing on a network device, a request, from a first service of a plurality of services, for use of a physical/hardware resource that connects a container running on the network device to a network. The request from the first service has a particular priority. The plurality of services execute within the container. The method further includes determining whether to connect the first service to the network via the physical/hardware resource based on the priority and an availability status of the physical/hardware resource and establishing, at a kernel level, a connection between the first service and the physical/hardware resource based on the determining.

Abstract: Techniques for access point (AP) based location computation are disclosed. A target wireless AP, communicatively coupled to a wireless station (STA), is identified. One or more location buddy APs, relating to the target AP, are identified based on the physical locations of the location buddy APs and the target AP. It is determined that a first location buddy AP, of the one or more location buddy APs, is communicatively coupled to the STA, and in response a location of the STA is determined using the target AP.

Abstract: Aggregated health information for a managed network may be retrieved and processed in response to changes to the managed network topology, configuration, or software. In response to receiving notification that a change to a component of the managed network has occurred, a change audit analysis engine can retrieve performance indicator information from components along a traceroute including the component which underwent the change. The retrieved performance indicator information can be processed by a memory based neural network to predict an impact of the change on the aggregated health of the managed network. The predicted impact can be compared to network health information retrieved through an ongoing basis and issues can be determined based on a comparison of the predict impact and the retrieved health information.

Abstract: Techniques for access point (AP) based location computation are disclosed. A target wireless AP, communicatively coupled to a wireless station (STA), is identified. One or more location buddy APs, relating to the target AP, are identified based on the physical locations of the location buddy APs and the target AP. It is determined that a first location buddy AP, of the one or more location buddy APs, is communicatively coupled to the STA, and in response a location of the STA is determined using the target AP.

Abstract: In one embodiment, a supervisory service for a wireless network obtains frequency-time Doppler profile information for an endpoint node attached to a first access point in the wireless network. The supervisory service uses the frequency-time Doppler profile information for the endpoint node as input to a machine learning model. The machine learning model is trained to output an action for the endpoint node with respect to the wireless network. The supervisory service causes the action for the endpoint node with respect to the wireless network to be performed.

Abstract: Aggregated health information for a managed network may be retrieved and processed in response to changes to the managed network topology, configuration, or software. In response to receiving notification that a change to a component of the managed network has occurred, a change audit analysis engine can retrieve performance indicator information from components along a traceroute including the component which underwent the change. The retrieved performance indicator information can be processed by a memory based neural network to predict an impact of the change on the aggregated health of the managed network. The predicted impact can be compared to network health information retrieved through an ongoing basis and issues can be determined based on a comparison of the predict impact and the retrieved health information.

Abstract: In one embodiment, a device receives data regarding a wireless client attempting to form an association with a particular wireless access point in a network. The device queries a blockchain ledger in a blockchain database for wireless roaming data regarding the wireless client. The device uses, when available from the blockchain ledger, the roaming data regarding the wireless client to complete the association between the wireless client and the particular wireless access point. The device updates the blockchain ledger with data regarding the association between the wireless client and the particular wireless access point.

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.

Abstract: Aggregated health information for a managed network may be retrieved and processed in response to changes to the managed network topology, configuration, or software. In response to receiving notification that a change to a component of the managed network has occurred, a change audit analysis engine can retrieve performance indicator information from components along a traceroute including the component which underwent the change. The retrieved performance indicator information can be processed by a memory based neural network to predict an impact of the change on the aggregated health of the managed network. The predicted impact can be compared to network health information retrieved through an ongoing basis and issues can be determined based on a comparison of the predict impact and the retrieved health information.

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.

Abstract: In one embodiment, a device obtains telemetry data indicative of channel usage by a plurality of Bluetooth Low Energy (BLE) tags. The device also obtains tag characteristic data indicative of one or more characteristics of the BLE tags. The device determines that usage of a particular channel by the BLE tags exceeds a predefined threshold, based on the obtained telemetry data. The device selects a subset of the plurality of BLE tags, based on their one or more characteristics indicated by the obtained tag characteristic data. The device instructs, for each of the selected subset of BLE tags, the selected BLE tag to increase an interval of time between BLE advertisements sent by the selected BLE tag.

Abstract: In one embodiment, a device obtains telemetry data indicative of channel usage by a plurality of Bluetooth Low Energy (BLE) tags. The device also obtains tag characteristic data indicative of one or more characteristics of the BLE tags. The device determines that usage of a particular channel by the BLE tags exceeds a predefined threshold, based on the obtained telemetry data. The device selects a subset of the plurality of BLE tags, based on their one or more characteristics indicated by the obtained tag characteristic data. The device instructs, for each of the selected subset of BLE tags, the selected BLE tag to increase an interval of time between BLE advertisements sent by the selected BLE tag.

Abstract: In one embodiment, a device receives data regarding a wireless client attempting to form an association with a particular wireless access point in a network. The device queries a blockchain ledger in a blockchain database for wireless roaming data regarding the wireless client. The device uses, when available from the blockchain ledger, the roaming data regarding the wireless client to complete the association between the wireless client and the particular wireless access point. The device updates the blockchain ledger with data regarding the association between the wireless client and the particular wireless access point.