US 11,909,618 B2
Actively learning PoPs to probe and probing frequency to maximize application experience predictions
Vinay Kumar Kolar, San Jose, CA (US); Jean-Philippe Vasseur, Saint Martin d'Uriage (FR); and Michal Wladyslaw Garcarz, Cracow (PL)
Assigned to CISCO TECHNOLOGY, INC., San Jose, CA (US)
Filed by Cisco Technology, Inc., San Jose, CA (US)
Filed on Apr. 6, 2022, as Appl. No. 17/714,483.
Prior Publication US 2023/0327971 A1, Oct. 12, 2023
Int. Cl. H04L 43/12 (2022.01); H04L 47/2475 (2022.01)
CPC H04L 43/12 (2013.01) [H04L 47/2475 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method comprising:
computing, by a device and for each of a set of points of presence via which traffic for an online application can be sent from a location, application experience metrics predicted for the online application over time;
assigning, by the device and for each of the set of points of presence, weights to different time periods, based on measures of uncertainty associated with the application experience metrics predicted for the online application over time;
generating, by the device and based on the weights assigned to the different time periods for each of the set of points of presence, schedules for probing network paths connecting the location to the online application via the set of points of presence, wherein the schedules allocate probing across the different time periods and network paths, to maximize their corresponding weights, given one or more probe quotas specified via a user interface; and
causing, by the device, the network paths to be probed in accordance with their schedules, wherein results of this probing are used to select a particular point of presence from among the set of points of presence via which traffic for the online application should be sent from the location during a certain time period.