A method for providing data to a client computing device from an edge computing device is discussed herein. The method may include performing a network proximity check regarding the client computing device associated with a request for data captured by the wideband sensor. The method may further include determining, based on at least one proximity metric associated with the client computing device, a route for data responsive to the request for data associated with the network proximity check, where the route is one of a route including the cloud storage or a route that does not include the cloud storage. The method may also include receiving the request for data captured by the wideband sensor associated with the network proximity check. The method may also include transmitting the data responsive to the request for data captured by the wideband sensor associated with the network proximity check to the client computing device through the determined route.

Non-limiting examples of the present disclosure describe proactive detection and notification of a potential loss of connectivity during an electronic collaborative communication. Subsequently, a state of the electronic collaborative communication is managed to improve, among other technical advantages, processing efficiency of associated computing devices and users involved in the electronic collaborative communication. A quality of a network feed for a participant in an electronic collaborative communication is identified and analyzed to generate a warning of potential loss of connectivity. A state of an electronic collaborative communication is managed relative to a continued quality evaluation of said network feed. For example, a network feed may be placed on hold and/or a communication suspended due to potential connectivity issues. A representation of a video feed may be updated and/or a communication resumed when a quality of that video feed is determined to satisfy a pre-selected quality threshold.

Systems and methods are provided for determining in real-time geographic areas having a threshold level of content consumption and deploying dynamic geo-fences to contain these geographic areas. These dynamic geo-fences provide a means for timing message notifications in order to optimize the chances of delivering targeted content to a mobile device user based on the current geographic location of the user's device relative to a threshold level of content consumption area. As mobile device users may be more likely to launch a client application in a place where other users are currently consuming content, a general message notification sent to the user's device located in a dynamic geo-fence created based on real-time content consumption, may increase the likelihood that the user will launch the client application and thereby, allow targeted content to be delivered to the user's mobile device.

Systems and methods are provided for determining in real-time geographic areas having a threshold level of content consumption and deploying dynamic geo-fences to contain these geographic areas. These dynamic geo-fences provide a means for timing message notifications in order to optimize the chances of delivering targeted content to a mobile device user based on the current geographic location of the user's device relative to a threshold level of content consumption area. As mobile device users may be more likely to launch a client application in a place where other users are currently consuming content, a general message notification sent to the user's device located in a dynamic geo-fence created based on real-time content consumption, may increase the likelihood that the user will launch the client application and thereby, allow targeted content to be delivered to the user's mobile device.

The subject matter described herein includes methods, systems, and computer readable media for proactive network testing. One method for proactive network testing includes receiving, by a test controller and via a network tap, at least one metric associated with live network traffic; determining, by the test controller and using the at least one metric and a threshold value associated with the at least one metric, that a network test is to be performed; configuring, by the test controller, a first test agent to execute the network test; and executing, by the first test agent, the network test.

The subject matter described herein includes methods, systems, and computer readable media for proactive network testing. One method for proactive network testing includes receiving, by a test controller and via a network tap, at least one metric associated with live network traffic; determining, by the test controller and using the at least one metric and a threshold value associated with the at least one metric, that a network test is to be performed; configuring, by the test controller, a first test agent to execute the network test; and executing, by the first test agent, the network test.

Identifying Internet of Things (IoT) devices with packet flow behavior including by using machine learning models is disclosed. Information associated with a network communication of an IoT device is received. A determination of whether the IoT device has previously been classified is made. In response to determining that the IoT device has not previously been classified, a determination is made that a probability match for the IoT device against a behavior signature exceeds a threshold. Based at least in part on the probability match, a classification of the IoT device is provided to a security appliance configured to apply a policy to the IoT device.

A network device includes a network interface for establishing a communication session with another network device, a memory to store instructions, and a processor to execute the instructions. The processor may, for each time period during the communication session, adjust a size of a receive buffer of a socket. When the processor adjusts the size, the processor, if a utilization number of the receive buffer is greater than a high threshold: may determine a first new size for the receive buffer, and set a size of the receive buffer to the first new size. If the utilization number is less than a low threshold, the processor may determine a second new size for the receive buffer; and set the size of the receive buffer to the second new size.

A network device includes a network interface for establishing a communication session with another network device, a memory to store instructions, and a processor to execute the instructions. The processor may, for each time period during the communication session, adjust a size of a receive buffer of a socket. When the processor adjusts the size, the processor, if a utilization number of the receive buffer is greater than a high threshold: may determine a first new size for the receive buffer, and set a size of the receive buffer to the first new size. If the utilization number is less than a low threshold, the processor may determine a second new size for the receive buffer; and set the size of the receive buffer to the second new size.

Approaches presented herein enable deploying a distributed machine learning framework in an edge computing environment. More specifically, a status of a connection between a computing system and an edge node of a plurality of edge nodes is monitored. At least one server node and a group of worker nodes from the plurality of edge nodes are identified based on the status. A path for distributing the training data to the worker nodes is determined based on the status. The training data from the edge node to the worker nodes is distributed via the path.