An anomaly detection method includes collecting and preprocessing time series data every preset detection cycle; detecting an anomaly in time series data preprocessed for a current detection cycle using a deep learning model trained with an unsupervised learning scheme using features of time series data of a previous detection cycle; retraining the deep learning model by further using the time series data preprocessed for at least one detection cycle included in the current learning cycle; and detecting an anomaly in time series data collected and preprocessed for a detection cycle after the current learning cycle using the retrained deep learning model.

Graphical user interfaces are generated that, when displayed, provide a visual and interactive representation of one or more aspects associated with the execution of one or more applications on a computer network. The graphical user interfaces may in include graphical depictions representation policy objects, each policy object assigned one or more tags, each tag assigned to a category or a sub-category. The tags, when taken in combination, may identify an application, and one or more other characteristics associated with each of the policy objects. The graphical elements representing the policy objects may be displayed in the graphical user interfaces so that the policy objects assigned to tags in a category are positioned in an outer ring, and policy objects assigned to sub-category tags are positioned in a inner ring surrounded by the outer ring, with interconnection elements representing communications between policy objects extending within an interior area.

Graphical user interfaces are generated that, when displayed, provide a visual and interactive representation of one or more aspects associated with the execution of one or more applications on a computer network. The graphical user interfaces may in include graphical depictions representation policy objects, each policy object assigned one or more tags, each tag assigned to a category or a sub-category. The tags, when taken in combination, may identify an application, and one or more other characteristics associated with each of the policy objects. The graphical elements representing the policy objects may be displayed in the graphical user interfaces so that the policy objects assigned to tags in a category are positioned in an outer ring, and policy objects assigned to sub-category tags are positioned in a inner ring surrounded by the outer ring, with interconnection elements representing communications between policy objects extending within an interior area.

One or more network tests for a network are selected, wherein the selected one or more network tests simulate an attempt to establish an anomalous network configuration. A network configuration update is generated based on the selected one or more network tests and the network configuration update is issued to a network-based device. A performance of the network is monitored for establishment of the anomalous network configuration in response to the network configuration update and a configuration of the network is revised based on the monitored performance of the network, to mitigate the establishment of the anomalous network configuration.

A mobile dashboard for automated contact center testing gives up-to-the-minute status reports on your customer experience, enabling you to make operational decisions and drill down to the source of an issue while on the go. A mobile-optimized executive dashboard display can be customized for each unique user, so business and technical stakeholders can filter the display for the customer experience (CX) metrics that are most relevant to them, and configure push notification alerts accordingly.

One or more computer processors generate a network fractal based on one or more predicted network conditions for a network that includes a change in user density, user device latency, and network throughput, wherein the network fractal is a deployment template comprised of a plurality of nodes. The one or more computer processors select a configuration of network infrastructure devices placed at each node in the plurality of nodes comprised in the generated network fractal. The one or more computer processors modify the network utilizing the generated network fractal and the selected configuration of network infrastructure devices. The one or more computer processors deploy the modified network.

One or more computer processors generate a network fractal based on one or more predicted network conditions for a network that includes a change in user density, user device latency, and network throughput, wherein the network fractal is a deployment template comprised of a plurality of nodes. The one or more computer processors select a configuration of network infrastructure devices placed at each node in the plurality of nodes comprised in the generated network fractal. The one or more computer processors modify the network utilizing the generated network fractal and the selected configuration of network infrastructure devices. The one or more computer processors deploy the modified network.

A system for validating automated vehicle data transmission capabilities of a vehicle is provided. The system includes a vehicle data transmission diagnostics (VDTD) server in communication with the vehicle and a plurality of roadside evaluation units. The VDTD server includes at least one processor and at least one memory device, and is programmed to: (i) determine that a data latency risk evaluation (DLRE) should be performed for the vehicle, (ii) transmit a DLRE request to the vehicle, (iii) receive, from the vehicle, a response to the transmitted DLRE request including trip data, the trip data including a selected route to be taken by the vehicle, (iv) interrogate the plurality of roadside evaluation units based upon the received trip data, and (v) select, based upon the interrogation, one of the plurality of roadside evaluation units to be a data latency evaluation checkpoint for the vehicle during the upcoming trip.

Technologies for dynamic accelerator selection include a compute sled. The compute sled includes a network interface controller to communicate with a remote accelerator of an accelerator sled over a network, where the network interface controller includes a local accelerator and a compute engine. The compute engine is to obtain network telemetry data indicative of a level of bandwidth saturation of the network. The compute engine is also to determine whether to accelerate a function managed by the compute sled. The compute engine is further to determine, in response to a determination to accelerate the function, whether to offload the function to the remote accelerator of the accelerator sled based on the telemetry data. Also the compute engine is to assign, in response a determination not to offload the function to the remote accelerator, the function to the local accelerator of the network interface controller.

Some embodiments provide a network forwarding integrated circuit (IC) that includes at least one packet processing pipeline. The packet processing pipeline includes multiple match-action stages, at least one of which includes a stateful processing unit that operates at a line rate of the network forwarding IC. The stateful processing unit is configured to receive data stored in a memory location associated with a stateful table of the match-action stage. The data includes a set of values. The stateful processing unit is further configured to identify one of a maximum value and a minimum value from the set of values, and to output the identified value for use by a next match-action stage.