Cisco Technology, Inc.

Mehdi Nikkhah

Xiaoqing Zhu

Rob Liston

Dan Tan

John George Apostolopoulos

Santosh Ghanshyam Pandey

Herbert Michael Wildfeuer

Wai-Tian Tan

Preethi Natarajan

Mark Allen Webb

Mahyar Fazlyab

Wai-tan Tan

Yaqi Wang

In one embodiment, a service receives machine learning-based generative models from a plurality of distributed sites. Each generative model is trained locally at a site using unlabeled data observed at that site to generate synthetic unlabeled data that mimics the unlabeled data used to train the generative model. The service receives, from each of the distributed sites, a subset of labeled data observed at that site. The service uses the generative models to generate synthetic unlabeled data. The service trains a global machine learning-based model using the received subsets of labeled data received from the distributed sites and the synthetic unlabeled data generated by the generative models.

Communication efficient machine learning of data across multiple sites

A server uses an LSTM neural network to predict a bandwidth value for a computer network element using past traffic data. The server receives a time series of bandwidth utilization of the computer network element. The time series includes bandwidth values associated with a respective time values. The LSTM neural network is trained with a training set selected from at least a portion of the time series. The server generates a predicted bandwidth value associated with a future time value based on the LSTM neural network. The provisioned bandwidth for the computer network element is adjusted based on the predicted bandwidth value.

Network traffic prediction using long short term memory neural networks

In one embodiment, a device determines locations of a plurality of transmitters relative to a particular wireless access point in a wireless network. One of the transmitters comprises a target client to which the particular wireless access point is to communicate. The device compares a plurality of beamforming patterns associated with the particular wireless access point to the determined locations. The device selects, based on the comparison, one of the beamforming patterns for use by the particular wireless access point to communicate with the target client. The device controls the particular wireless access point to use the selected beamforming pattern to communicate with the target client.

Wireless beamforming optimization using client location information

A network computing device determines a network topology for at least one network flow path between at least one ingress network border device and at least one egress network border device. The network computing device receives a message containing data indicating flow statistics for the at least one ingress network border device. The network computing device generates flow statistics for at least one network device along the at least one network flow path from the network topology and the flow statistics for the at least one ingress network border device. The network computing device generates the flow statistics for at least one network device along the at least one network flow path without receiving flow statistics from the at least one network device along the at least one network flow path.

Network statistics estimation and prediction

A methodology includes determining coarse location coordinates for a mobile device, anchoring the coarse location coordinates to a map, receiving inertial measurement unit data supplied by the mobile device, wherein the inertial measurement unit data is indicative of relative location coordinates of the mobile device, generating an unanchored path of the mobile device based on the relative location coordinates, and anchoring the unanchored path of the mobile device to the map in a position that optimizes a match between the coarse location coordinates and the relative location coordinates of the mobile device.

Collaborative localization between phone and infrastructure

A methodology includes receiving from a first mobile device a first estimated location of the first mobile device and a first estimated error associated with the first estimated location, the first estimated location being based on first coarse data from a first wireless access point location determination system fused with inertial measurement unit (IMU) data from the first mobile device, receiving from a second mobile device a second estimated location of the second mobile device and a second estimated error associated with the second estimated location, the second estimated location being based on second coarse data from the first wireless access point location determination system fused with IMU data from the second mobile device, and based on the first estimated error and the second estimated error, determining a recommended change to a deployment of a wireless access point associated with the first wireless access point location determination system.

Growing community of inventors

Belmont, CA, United States of America

Mehdi Nikkhah