Wi-Fi Analytics

Rich insights into Wi-Fi device performance with unique cross-stack data analysis

Voyance analyzes and provides insight into all aspects of mobile client user experience. An extremely important but complex portion of this includes both the RF component as well as as the connectivity portion which includes RADIUS, DHCP, ARP and DNS services.

Measuring client wireless

Detailed,real-time metrics are collected from wireless infrastructure elements including: the wireless LAN controller, individual access points,and client devices running the Voyance Client. These real-time metrics give extraordinary insight into both the infrastructure as well as a client’s wireless state, such as its location and associated AP radio, signal strength and layer 2 retransmissions, noise levels and co-channel interference seen, etc. All of these metrics are collected for every client device as well as every infrastructure element and combined with the information to compute every client’s wireless experience at any point in time. This information is summarized in terms of “client hours” of poor wireless performance that each client experiences and is also used for correlations of different attributes and root cause analysis.

Connectivity Analytics

Rich metrics are collected that describe the detailed protocol interactions of RADIUS, DHCP, ARP and DNS from the wireless Controller. This data is gathered from crawlers performing DPI, from client devices running the Voyance Client, and from SYSLOG data sent to the system from the servers involved.  All of these metrics for every client device are collected and combined to compute every client’s connectivity experience at any point in time. All information is summarized in terms of “client hours” of poor connectivity performance that each client experiences.

Recommendation Engine

Observations of historical data are used to make predictive recommendations like: “if you take action X, you will get a benefit of Y”. Examples of these actions might be “if you alleviate co-channel interference near this access point BLDG1_FL4_AP2 caused by these interfering APs, you can improve wireless user experience by 300 client hours”.  Additional information is correlated with each recommendation to give even more starting points for further exploration.

Multi-Dimensional Analysis

Insights are provided into extremely granular complex questions, such as “Which Windows laptop users on the corporate SSID are experiencing poor coverage issues on the 3rd floor of building 2”. The system correlates, indexes and visualizes every client incident across the relevant dimensions such as root cause, location, SSID, VLAN, OS, etc.

Root Cause Correlation

Relevant data sources that apply to root cause of a particular type of incident are all correlated.This allows enterprises to systematically call out or eliminate potential root causes. For instance, if a client has poor wireless experience, the system automatically correlates symptoms including whether it roamed recently, or whether the AP its was connected has high co-channel interference, noise levels, numbers of clients, etc. Similarly, when a client experiences high latency connecting to RADIUS, the system will correlate the CPU and memory utilization of the server, the reason code offered, etc.

Map-based Visualization

All problems are visualized on different types of maps depending on the context. If a user wants to see performance comparisons between global sites or between buildings within a campus, the visualization is performed on a world/street map. Conversely, if a user is focused on looking at detailed issues within a floor of a building, detailed visualizations are provided on a floor map.

Client Roaming Analysis

Information about client roaming is collected from the wireless controller by crawlers performing DPI,from client devices running the Voyance Client, as well as from SYSLOG data sent to the system from various servers. Using these data points, the system immediately determines where poor roams causing session drops are occurring.  Additionally, client perspective data about roaming is correlated with the infrastructure perspective data about the client.