Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. Edge clusters and an intelligent routing module route traffic to applications executing in the cloud computing platform. Cost and latency may be managed by the intelligent routing module by routing requests over the Internet or a cloud backbone network and using or bypassing cloud POPs. The placement of edge clusters may be selected according to measured or estimated latency. Latency may be estimated using speed test servers and the locations of speed test servers may be verified.

Abstract: Systems, methods, and computer-readable media may determine location-related data for a plurality of access points located in an area in communication with a network by determining that a plurality of access points in a network are associated with a same geographical area including identifying, from among the plurality of access points, a first access point associated with the geographical area, determining first location-related data for the first access point, determining second location-related data for a second access point of the plurality of access points, the second access point being interior to the first access point within the network based at least in part on a determination that the second access point has at least a threshold number of the neighbor access points, and exchanging ranging data indicative of a first relative distance between the first access point and the second access point, the ranging data based at least in part on ranging message exchange measurements.

Abstract: Segments of a network having connectivity issues are detected in a network environment that may include one or more cloud computing platforms. A mutual information algorithm is used to determine relevance of network element factors, a subset of factors are selected based on relevance, and clustered according to values for the subset of factors, and quality of the clusters evaluated. Various thresholds for selecting the subset of factors may be used to determine which provides improved cluster quality. An approach for performing root cause analysis of events in a network environment selects bad events for logging alerts based on whether a factor is found to distinguish bad events according to a mutual information algorithm. Events for alerts maybe aggregated based on temporal proximity or similarity. Visualization may be performed using Sankey diagrams with each column representing a factor.

Abstract: Segments of a network having connectivity issues are detected in a network environment that may include one or more cloud computing platforms. A mutual information algorithm is used to determine relevance of network element factors, a subset of factors are selected based on relevance, and clustered according to values for the subset of factors, and quality of the clusters evaluated. Various thresholds for selecting the subset of factors may be used to determine which provides improved cluster quality. An approach for performing root cause analysis of events in a network environment selects bad events for logging alerts based on whether a factor is found to distinguish bad events according to a mutual information algorithm. Events for alerts maybe aggregated based on temporal proximity or similarity. Visualization may be performed using Sankey diagrams with each column representing a factor.

Abstract: Access Point (AP) placement using Fine Time Measurement (FTM) may be provided. First, a plurality of Time-of-Flight (ToF) values between a first service end point and a second service end point may be determined. Each one of the plurality of ToF values may be derived from packets transmitted via different beamforming vector patterns at the first service end point and the second service end point. Then a minimum ToF value of the plurality of ToF values may be determined. Next, a distance between the first service end point and the second service end point may be determined based on the minimum ToF value.

Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

Abstract: Systems, methods, and computer-readable media may determine location-related data for a plurality of access points located in an area in communication with a network by determining that a plurality of access points in a network are associated with a same geographical area including identifying, from among the plurality of access points, a first access point associated with the geographical area, determining first location-related data for the first access point, determining second location-related data for a second access point of the plurality of access points, the second access point being interior to the first access point within the network based at least in part on a determination that the second access point has at least a threshold number of the neighbor access points, and exchanging ranging data indicative of a first relative distance between the first access point and the second access point, the ranging data based at least in part on ranging message exchange measurements.

Abstract: In one embodiment, a network assurance service that monitors a wireless network identifies a set of wireless network anomalies detected in the wireless network that are associated with a set of one or more network measurements. The network assurance service classifies the set of wireless anomalies as radio-related or backend-related. The network assurance service, when the set of wireless anomalies are classified as radio-related, determines that the wireless anomalies are recurring for a particular wireless access point in the network. The network assurance service initiates a change to the wireless network in part to move clients in the wireless network from the particular wireless access point to another wireless access point in the network.

Abstract: A system may be configured to identify from among a plurality of access points in an area in communication with a network, a first access point associated with the area. In addition the system may also be configured to determine first location-related data for the first access point. Determining second location-related data for a second access point of the plurality of access points, the second access point being interior to the first access point within the network includes exchanging ranging data indicative of a first relative distance between the first access point and the second access point, the ranging data based at least in part on ranging message exchange measurements, and communicating the first location-related data from the first access point to the second access point.

Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. Edge clusters and an intelligent routing module route traffic to applications executing in the cloud computing platform. Cost and latency may be managed by the intelligent routing module by routing requests over the Internet or a cloud backbone network and using or bypassing cloud POPs. The placement of edge clusters may be selected according to measured or estimated latency. Latency may be estimated using speed test servers and the locations of speed test servers may be verified.

Abstract: Access Point (AP) placement using Fine Time Measurement (FTM) may be provided. First, a plurality of Time-of-Flight (ToF) values between a first service end point and a second service end point may be determined. Each one of the plurality of ToF values may be derived from packets transmitted via different beamforming vector patterns at the first service end point and the second service end point. Then a minimum ToF value of the plurality of ToF values may be determined. Next, a distance between the first service end point and the second service end point may be determined based on the minimum ToF value.

Abstract: Offloading of location computation from a location server to an access point through the use of projections on base phase vectors may be provided. First, an Access Point (AP) may receive a set of two or more base phase vectors from a location server. Next, the AP may measure a measured phase vector for a first signal from a user device. Then, the AP can determine projection values based on a comparison of the measured phase vector to each base phase vector. From these comparisons, the AP can determine a subset of base phase vectors with the highest projection values. The AP can then send the projection values and the subset of base phase vectors to the location server, wherein the location server determines the device location from these projection values and subset of base phase vectors.

Abstract: In one embodiment, a network assurance service that monitors a network maps time series of values of key performance indicator (KPIs) measured from the network to lists of unique values from the time series. The service sets a target alarm rate for anomaly detection alarms raised by the network assurance service. The service uses an optimization function to identify a set of thresholds for the KPIs. The optimization function is based on: a comparison between the target alarm rate and a fraction of network issues flagged by the service as outliers, KPI thresholds selected based on the lists of unique values from the time series, and a number of thresholds that the KPIs must cross for the service to raise an alarm. The service raises an anomaly detection alarm for the monitored network based on the identified set of thresholds for the KPIs.

Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. Edge clusters and an intelligent routing module route traffic to applications executing in the cloud computing platform. Cost and latency may be managed by the intelligent routing module by routing requests over the Internet or a cloud backbone network and using or bypassing cloud POPs. The placement of edge clusters may be selected according to measured or estimated latency. Latency may be estimated using speed test servers and the locations of speed test servers may be verified.

Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. Edge clusters and an intelligent routing module route traffic to applications executing in the cloud computing platform. Cost and latency may be managed by the intelligent routing module by routing requests over the Internet or a cloud backbone network and using or bypassing cloud POPs. The placement of edge clusters may be selected according to measured or estimated latency. Latency may be estimated using speed test servers and the locations of speed test servers may be verified.

Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. Edge clusters and an intelligent routing module route traffic to applications executing in the cloud computing platform. Cost and latency may be managed by the intelligent routing module by routing requests over the Internet or a cloud backbone network and using or bypassing cloud POPs. The placement of edge clusters may be selected according to measured or estimated latency. Latency may be estimated using speed test servers and the locations of speed test servers may be verified.

Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. Edge clusters and an intelligent routing module route traffic to applications executing in the cloud computing platform. Cost and latency may be managed by the intelligent routing module by routing requests over the Internet or a cloud backbone network and using or bypassing cloud POPs. The placement of edge clusters may be selected according to measured or estimated latency. Latency may be estimated using speed test servers and the locations of speed test servers may be verified.

Abstract: Access Point (AP) placement using Fine Time Measurement (FTM) may be provided. First, a plurality of Time-of-Flight (ToF) values between a first service end point and a second service end point may be determined. Each one of the plurality of ToF values may be derived from packets transmitted via different beamforming vector patterns at the first service end point and the second service end point. Then a minimum ToF value of the plurality of ToF values may be determined. Next, a distance between the first service end point and the second service end point may be determined based on the minimum ToF value.

Abstract: Edge clusters execute in a plurality of regional clouds of a cloud computing platforms, which may include cloud POPs. Edge clusters may be programmed to control access to applications executing in the cloud computing platform. Edge clusters and an intelligent routing module route traffic to applications executing in the cloud computing platform. Cost and latency may be managed by the intelligent routing module by routing requests over the Internet or a cloud backbone network and using or bypassing cloud POPs. The placement of edge clusters may be selected according to measured or estimated latency. Latency may be estimated using speed test servers and the locations of speed test servers may be verified.

Abstract: Client analytics-driven dynamic channel assignment may be provided. At a client, Radio Frequency (RF) channels may be scanned to detect access points of a network. A subset of the access points belonging to a same Extended Set Service (ESS) of the network may be determined based on data collected from the access points during the scan. For each access point in the subset, a Channel Quality Index (CQI) may be measured for an RF channel assigned to the access point, and a non-preferred channel report may be generated based on the CQI. The non-preferred channel report may be transmitted from the client to a network management system for use in dynamic channel assignment.