Abstract: In one embodiment, a device receives observed access point (AP) features of one or more APs in a monitored network. The device clusters the observed AP features within a latent space to form AP feature clusters. The device applies labels to the AP feature clusters within the latent space. The device uses the applied labels to the AP feature clusters to describe future behaviors of the one or more APs in the monitored network.

Abstract: In one embodiment, a service receives telemetry data collected from a plurality of different networks. The service combines the telemetry data into a synthetic input trace. The service inputs the synthetic input trace into a plurality of machine learning models to generate a plurality of predicted key performance indicators (KPIs), each of the models having been trained to assess telemetry data from an associated network in the plurality of different networks and predict a KPI for that network. The service compares the plurality of predicted KPIs to identify one of the plurality of different networks as exhibiting an abnormal behavior.

Abstract: The disclosed technology relates to a process of providing dynamic machine learning on premise model selection. In particular, a set of machine learned models are generated and provided to an on premise computing device. The machine learned models are generated using a cluster of customer data (e.g. telemetric data) stored on a computing network having different ranges of computational complexity. One of the machine learned models from the set of machine learned models will be selected based on the current available computational resources detected at the on premise computing device. Different machine learned models from the set of machine learned models can then be selected based on changes in the available computational resources and/or customer feedback.

Abstract: The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization; define a first period prior to the temporal event and a second period posterior to the temporal event; and compare network data collected in the first period and network data collected in the second period.

Abstract: The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization. The technology defines, based on a nature of the temporal event, a first period prior to the temporal event or a second period posterior to the temporal event. The technology compares network data collected in the first period and network data collected in the second period.

Abstract: In one embodiment, a device receives data regarding usage of access points in a network by a plurality of clients in the network. The device maintains an access point graph that represents the access points in the network as vertices of the access point graph. The device generates, for each of the plurality of clients, client trajectories as trajectory subgraphs of the access point graph. A particular client trajectory for a particular client comprises a set of edges between a subset of the vertices of the access point graph and represents transitions between access points in the network performed by the particular client. The device identifies a transition pattern from the client trajectories by deconstructing the trajectory subgraphs. The device uses the identified transition pattern to effect a configuration change in the network.

Abstract: Technologies for dynamically generating topology and location based network insights are provided. In some examples, a method can include determining statistical changes in time series data including a series of data points associated with one or more conditions or parameters of a network; determining a period of time corresponding to one or more of the statistical changes in the time series data; obtaining telemetry data corresponding to a segment of the network and one or more time intervals, wherein a respective length of each time interval is based on a length of the period of time corresponding to the one or more of the statistical changes in the time series data; and generating, based on the telemetry data, insights about the segment of the network, the insights identifying a trend or statistical deviation in a behavior of the segment of the network during the one or more time intervals.

Abstract: In one embodiment, a network assurance service that monitors a network detects a network issue in the network using a machine learning model and based on telemetry data captured in the network. The service assigns the detected network issue to an issue cluster by applying clustering to the detected network issue and to a plurality of previously detected network issues. The service selects a set of one or more actions for the detected network issue from among a plurality of actions associated with the previously detected network issues in the issue cluster. The service obtains context data for the detected network issue. The service provides, to a user interface, an indication of the detected network issue, the obtained context data for the detected network issue, and the selected set of one or more actions.

Abstract: In one embodiment, a service receives telemetry data collected from a plurality of different networks. The service combines the telemetry data into a synthetic input trace. The service inputs the synthetic input trace into a plurality of machine learning models to generate a plurality of predicted key performance indicators (KPIs), each of the models having been trained to assess telemetry data from an associated network in the plurality of different networks and predict a KPI for that network. The service compares the plurality of predicted KPIs to identify one of the plurality of different networks as exhibiting an abnormal behavior.

Abstract: In one embodiment, a device receives observed access point (AP) features of one or more APs in a monitored network. The device clusters the observed AP features within a latent space to form AP feature clusters. The device applies labels to the AP feature clusters within the latent space. The device uses the applied labels to the AP feature clusters to describe future behaviors of the one or more APs in the monitored network.

Abstract: In one embodiment, possible voting nodes in a network are identified. The possible voting nodes each execute a classifier that is configured to select a label from among a plurality of labels based on a set of input features. A set of one or more eligible voting nodes is selected from among the possible voting nodes based on a network policy. Voting requests are then provided to the one or more eligible voting nodes that cause the one or more eligible voting nodes to select labels from among the plurality of labels. Votes are received from the eligible voting nodes that include the selected labels and are used to determine a voting result.

Abstract: Technologies for dynamically generating topology and location based network insights are provided. In some examples, a method can include determining statistical changes in time series data including a series of data points associated with one or more conditions or parameters of a network; determining a period of time corresponding to one or more of the statistical changes in the time series data; obtaining telemetry data corresponding to a segment of the network and one or more time intervals, wherein a respective length of each time interval is based on a length of the period of time corresponding to the one or more of the statistical changes in the time series data; and generating, based on the telemetry data, insights about the segment of the network, the insights identifying a trend or statistical deviation in a behavior of the segment of the network during the one or more time intervals.

Abstract: The present technology pertains to a system, method, and non-transitory computer-readable medium for evaluating the impact of network changes. The technology can detect a temporal event, wherein the temporal event is associated with a change in a network configuration, implementation, or utilization. The technology defines, based on a nature of the temporal event, a first period prior to the temporal event or a second period posterior to the temporal event. The technology compares network data collected in the first period and network data collected in the second period.

Abstract: The disclosed technology relates to a process of providing dynamic machine learning on premise model selection. In particular, a set of machine learned models are generated and provided to an on premise computing device. The machine learned models are generated using a cluster of customer data (e.g. telemetric data) stored on a computing network having different ranges of computational complexity. One of the machine learned models from the set of machine learned models will be selected based on the current available computational resources detected at the on premise computing device.

Abstract: In one embodiment, a device in a network receives information regarding a network anomaly detected by an anomaly detector deployed in the network. The device identifies the detected network anomaly as a false positive based on the information regarding the network anomaly. The device generates an output filter for the anomaly detector, in response to identifying the detected network anomaly as a false positive. The output filter is configured to filter an output of the anomaly detector associated with the false positive. The device causes the generated output filter to be installed at the anomaly detector.

Abstract: In one embodiment, a device receives observed access point (AP) features of one or more APs in a monitored network. The device clusters the observed AP features within a latent space to form AP feature clusters. The device applies labels to the AP feature clusters within the latent space. The device uses the applied labels to the AP feature clusters to describe future behaviors of the one or more APs in the monitored network.

Abstract: In one embodiment, a device receives data regarding usage of access points in a network by a plurality of clients in the network. The device maintains an access point graph that represents the access points in the network as vertices of the access point graph. The device generates, for each of the plurality of clients, client trajectories as trajectory subgraphs of the access point graph. A particular client trajectory for a particular client comprises a set of edges between a subset of the vertices of the access point graph and represents transitions between access points in the network performed by the particular client. The device identifies a transition pattern from the client trajectories by deconstructing the trajectory subgraphs. The device uses the identified transition pattern to effect a configuration change in the network.

Abstract: In one embodiment, a device receives data regarding usage of access points in a network by a plurality of clients in the network. The device maintains an access point graph that represents the access points in the network as vertices of the access point graph. The device generates, for each of the plurality of clients, client trajectories as trajectory subgraphs of the access point graph. A particular client trajectory for a particular client comprises a set of edges between a subset of the vertices of the access point graph and represents transitions between access points in the network performed by the particular client. The device identifies a transition pattern from the client trajectories by deconstructing the trajectory subgraphs. The device uses the identified transition pattern to effect a configuration change in the network.

Abstract: In one embodiment, a network assurance service receives data regarding a monitored network. The service analyzes the received data using a machine learning-based model, to perform a network assurance function for the monitored network. The service detects a lowered performance of the machine learning-based model when a performance metric of the machine learning-based model is below a threshold for the performance metric. When it is determined that the lowered performance of the machine-learning based model is correlated with the sample rate of the received data, the service adjusts the sample rate of the data.

Abstract: In one embodiment, a local service of a network reports configuration information regarding the network to a cloud-based network assurance service. The local service receives a classifier selected by the cloud-based network assurance service based on the configuration information regarding the network. The local service classifies, using the received classifier, telemetry data collected from the network, to select a modeling strategy for the network. The local service installs, based on the modeling strategy for the network, a machine learning-based model to the local service for monitoring the network.