Abstract: 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.

Abstract: Methods and systems for a deep learning based problem advisor are disclosed. A method includes: obtaining, by a computing device, a log file including events generated during execution of a software application; determining, by the computing device, at least one possible cause for a problem in the software application using the obtained log file and a knowledge base including calling paths for each of a plurality of methods in source code of the software application; for each of the at least one possible cause for the problem, the computing device simulating user actions in the software application; and determining, by the computing device, a root cause based on the simulating user actions in the software application.

Abstract: 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.

Abstract: Methods and systems for a deep learning based problem advisor are disclosed. A method includes: obtaining, by a computing device, a log file including events generated during execution of a software application; determining, by the computing device, at least one possible cause for a problem in the software application using the obtained log file and a knowledge base including calling paths for each of a plurality of methods in source code of the software application; for each of the at least one possible cause for the problem, the computing device simulating user actions in the software application; and determining, by the computing device, a root cause based on the simulating user actions in the software application.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In one embodiment, a device in a network assigns packets from a communication transmitted via the network to time windows over a period of time. The device determines a transmission performance metric for each of the packets in a particular time window and calculates, for each of the time windows, local disturbance scores, which are based on the transmission performance metrics for the packet in the time windows. A particular local disturbance score for a particular time window maps the transmission performance metrics for the packets in the time window to a perceived quality metric. The device determines a distortion score for the communication by aggregating the local disturbance scores for the time windows over the period of time.

Abstract: Large source data packets having large packet sizes and small source data packets having small packet sizes that are smaller than the large packet sizes are received. The small source data packets and the large source data packets are sent to a receiving device without forward error correction (FEC). The small source data packets are aggregated into a container packet having a header configured to differentiate the container packet from the large source data packets and the small source data packets. The large source data packets and the container packet are encoded with forward error correction to produce FEC-encoded packets to enable forward error correction of the large source data packets and the container packet at the receiving device. The FEC-encoded packets are sent to the receiving device.

Abstract: A user is provisioned for a Web service by supplying a user name and password. A digital certificate and VPN identifier are generated and downloaded to the user's computer. The VPN identifier and user identifier are stored into a database. The user accesses the Web service and establishes a VPN using the certificate and VPN identifier. A user identifier, user name or user password is not required. A gateway computer uses the VPN identifier to access the database previously established during the provisioning session to retrieve the user identifier. Retrieval of the user identifier validates that the computing device is authorized to use the Web service. The gateway computer stores the client IP address and a mapping to the user identifier into a database. A proxy server retrieves the user identifier from the database using the IP address and includes the user identifier in Web traffic for a remote computer.

Abstract: In one embodiment, a first device in a network sends a Session Traversal Utilities for Network Address Translation (STUN) binding request towards an endpoint device of a media session between the first and endpoint devices. The binding request includes one or more network attribute fields. The first device receives a binding response from an intermediate node between the first and endpoint devices in the network, in response to sending the binding request towards the endpoint device. The intermediate node inserted the one or more network attribute fields into the binding response. The received binding response includes one or more metrics for the media session in the one or more network attribute fields. The first device adjusts one or more bitrates of the media session based on the one or more metrics for the media session in the received binding response.

Abstract: An encoder receives a sequence of packets. For each packet, the encoder selects a window of at least previous packets in the sequence of packets. The encoder identifies in the window one or more earlier packets among the previous packets and one or more later packets separated from the one or more earlier packets by a gap including gap packets. The encoder encodes the one or more earlier packets and the one or more later packets into a forward error correction (FEC) packet corresponding to the packet, without using any of the gap packets, and transmits the FEC packet and the packet.

Abstract: Large source data packets having large packet sizes and small source data packets having small packet sizes that are smaller than the large packet sizes are received. The small source data packets and the large source data packets are sent to a receiving device without forward error correction (FEC). The small source data packets are aggregated into a container packet having a header configured to differentiate the container packet from the large source data packets and the small source data packets. The large source data packets and the container packet are encoded with forward error correction to produce FEC-encoded packets to enable forward error correction of the large source data packets and the container packet at the receiving device. The FEC-encoded packets are sent to the receiving device.

Abstract: In one embodiment, a first device in a network sends a Session Traversal Utilities for Network Address Translation (STUN) binding request towards an endpoint device of a media session between the first and endpoint devices. The binding request includes one or more network attribute fields. The first device receives a binding response from an intermediate node between the first and endpoint devices in the network, in response to sending the binding request towards the endpoint device. The intermediate node inserted the one or more network attribute fields into the binding response. The received binding response includes one or more metrics for the media session in the one or more network attribute fields. The first device adjusts one or more bitrates of the media session based on the one or more metrics for the media session in the received binding response.

Abstract: An encoder receives a sequence of packets. For each packet, the encoder selects a window of at least previous packets in the sequence of packets. The encoder identifies in the window one or more earlier packets among the previous packets and one or more later packets separated from the one or more earlier packets by a gap including gap packets. The encoder encodes the one or more earlier packets and the one or more later packets into a forward error correction (FEC) packet corresponding to the packet, without using any of the gap packets, and transmits the FEC packet and the packet.

Abstract: An endpoint computer includes a local client that transmits web traffic to a local proxy that also runs on the endpoint computer. The local proxy obtains a customer identity string that identifies a user of the local client as a paying customer of an SaaS scanning service provided by an SaaS scanning system. The local proxy inserts the customer identity string into the web traffic and thereafter transmits the web traffic to the SaaS scanning system, which authenticates the customer identity string before scanning the web traffic for web threats. The local client transmits the web traffic to the local proxy using a communication protocol and the local proxy can transmit the web traffic to the SaaS scanning system using the same or different communication protocol.

Abstract: In one embodiment, a device in a network identifies delay requirements of each of a plurality of media streams. The device selects a joint forward error correction (FEC) encoding strategy for the plurality of media streams based on the identified delay requirements of the streams and on a burst loss length of a communication channel. The device applies the selected joint FEC encoding strategy to the plurality of media streams, to form a multiplexed packet stream. The device sends the multiplexed packet stream to one or more nodes in the network via the communication channel.