Assessing the true impact of predictive application-driven routing on end user experience
In one embodiment, a supervisory service for a network obtains a first set of user experience survey data from users of an online application whose traffic was rerouted by a predictive routing engine. The supervisory service obtains a second set of user experience survey data from users of the online application whose traffic was not rerouted by the predictive routing engine. The supervisory service generates, using the first set of user experience survey data and the second set of user experience survey data, a user experience prediction model to predict user experience scores for the online application. The supervisory service adjusts the predictive routing engine using predicted user experience scores from the user experience prediction model.
1. A method comprising:
obtaining, by a supervisory service for a network, a first set of user experience survey data from a first set of users of an online application whose traffic was rerouted by a predictive routing engine;
obtaining, by the supervisory service, a second set of user experience survey data from a second set of users of the online application whose traffic was not rerouted by the predictive routing engine;
generating, by the supervisory service and using the first set of user experience survey data and the second set of user experience survey data, a user experience prediction model to predict user experience scores for the online application; and
adjusting, by the supervisory service, the predictive routing engine using predicted user experience scores from the user experience prediction model.
2. The method as in claim 1 , wherein the predictive routing engine reroutes traffic for the online application to avoid violations of a service level agreement (SLA) associated with the traffic.
3. The method as in claim 1 , wherein adjusting the predictive routing engine comprises:
adjusting the predictive routing engine to predict disruptions to traffic of the online application based on the user experience scores from the user experience prediction model.
4. The method as in claim 1 , wherein the predictive routing engine predicted disruptions to traffic of the second set of users of the online application whose traffic was not rerouted.
5. The method as in claim 1 , further comprising:
providing, by the supervisory service, data indicative of the first set of user experience survey data to a user interface.
6. The method as in claim 1 , wherein the first set of user experience survey data and the second set of user experience survey data are obtained at least in part from local agents executed by devices of the first and second sets of users.
7. The method as in claim 1 , wherein the first set of user experience survey data and the second set of user experience survey data are obtained at least in part from surveys conducted within the online application.
8. The method as in claim 1 , wherein the user experience prediction model is a machine learning-based model.
9. The method as in claim 1 , wherein the online application is a software as a service (SaaS) application.
10. The method as in claim 1 , wherein the network is a software defined network and wherein the predictive routing engine reroutes traffic for the online application via a controller for the software defined network.
11. An apparatus, comprising:
one or more network interfaces;
a processor coupled to the one or more network interfaces and configured to execute one or more processes; and
a memory configured to store a process that is executable by the processor, the process when executed configured to:
obtain a first set of user experience survey data from a first set of users of an online application whose traffic was rerouted by a predictive routing engine;
obtain a second set of user experience survey data from a second set of users of the online application whose traffic was not rerouted by the predictive routing engine;
generate, using the first set of user experience survey data and the second set of user experience survey data, a user experience prediction model to predict user experience scores for the online application; and
adjust the predictive routing engine using predicted user experience scores from the user experience prediction model.
12. The apparatus as in claim 11 , wherein the predictive routing engine reroutes traffic for the online application to avoid violations of a service level agreement (SLA) associated with the traffic.
13. The apparatus as in claim 11 , wherein the apparatus adjusts the predictive routing engine by:
adjusting the predictive routing engine to predict disruptions to traffic of the online application based on the user experience scores from the user experience prediction model.
14. The apparatus as in claim 11 , wherein the predictive routing engine predicted disruptions to traffic of the second set of users of the online application whose traffic was not rerouted.
15. The apparatus as in claim 11 , wherein the process when executed is further configured to:
provide data indicative of the first set of user experience survey data to a user interface.
16. The apparatus as in claim 11 , wherein the first set of user experience survey data and the second set of user experience survey data are obtained at least in part from local agents executed by devices of the first and second sets of users.
17. The apparatus as in claim 11 , wherein the first set of user experience survey data and the second set of user experience survey data are obtained at least in part from surveys conducted within the online application.
18. The apparatus as in claim 11 , wherein the online application is a software as a service (SaaS) application.
19. The apparatus as in claim 11 , wherein traffic for the online application is conveyed via a software defined network and wherein the predictive routing engine reroutes traffic for the online application via a controller for the software defined network.
20. A tangible, non-transitory, computer-readable medium storing program instructions that cause a supervisory service to execute a process comprising:
obtaining, by the supervisory service, a first set of user experience survey data from a first set of users of an online application whose traffic was rerouted by a predictive routing engine;
obtaining, by the supervisory service, a second set of user experience survey data from a second set of users of the online application whose traffic was not rerouted by the predictive routing engine;
generating, by the supervisory service and using the first set of user experience survey data and the second set of user experience survey data, a user experience prediction model to predict user experience scores for the online application; and
adjusting, by the supervisory service, the predictive routing engine using predicted user experience scores from the user experience prediction model.