A computer system automatically tests a network communication model by predicting whether particular traffic (whether actual or simulated) should be allowed on the network, and then estimating the accuracy of the network communication model based on the prediction. Such an estimate may be generated even before the model has been applied to traffic on the network. For example, steps can include observing positive data associated with a network; generating a network communication model based on the positive data; generating negative data based on the network communication model; calculating a precision of the network communication model based on the network communication model and the negative data; and calculating an accuracy of the network communication model based on one or more of the precision of the network communication model, or the network communication model and the positive data.

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.

A method is provided for identifying operating conditions of a system. Input data relating to operation of the system is applied to a multi-class model for classification, where the multi-class model is configured for classifying the data into one of a plurality of predefined classes, and each class corresponds to a respective operating condition of the system. A confidence level of the classification by the multi-class model is determined. If the confidence level is below a threshold confidence level, the input data is applied to a plurality of binary models, where each binary model is configured for determining whether the data is or is not in a respective one of the predefined classes. If the plurality of binary models determine that the data is not in any of the respective predefined classes, the data can be taken into consideration when updating the multi-class model.

Technologies for switching network traffic include a network switch. The network switch includes one or more processors and communication circuitry coupled to the one or more processors. The communication circuity is capable of switching network traffic of multiple link layer protocols. Additionally, the network switch includes one or more memory devices storing instructions that, when executed, cause the network switch to receive, with the communication circuitry through an optical connection, network traffic to be forwarded, and determine a link layer protocol of the received network traffic. The instructions additionally cause the network switch to forward the network traffic as a function of the determined link layer protocol. Other embodiments are also described and claimed.

A digital service evaluation system evaluates services and user sessions provided by a service, to provide an overall score of the service. The digital service evaluation system detects client sessions associated with one or more devices. The digital service evaluation system obtains a first plurality of scores associated with performance metrics of the client session, and calculates an overall score for the client session. The digital service evaluation system obtains a second plurality of scores and calculates a second overall score. The digital service evaluation system determines a weight for each performance metric based on the first and second plurality of scores and the overall scores. The digital service evaluation system uses the weights to determine which performance metric caused a change in the overall scores. The digital service evaluation system takes an action based on the determination that a performance metric caused a change in the overall scores.

The technologies described herein are generally directed to the autonomous onsite remediation of adverse conditions for network infrastructure in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include detecting a condition of a component of network equipment at a site that has a likelihood of indicating a defined adverse event that has at least a threshold likelihood of occurring. The method can further include, in response to detecting the condition, facilitating generating a graphical image of the component. Further, the method can include, based on information determined from the graphical image, remediating the condition.

An apparatus, method and computer program is described comprising: combining first features extracted from an echo signal using a convolutional encoder of a convolutional encoder-decoder having first weights, wherein the echo signal is obtained in response to a transmission over a channel or a simulation thereof; and using a convolutional decoder of the convolutional encoder-decoder to generate an estimate of a frequency response of the channel based on the echo signal, wherein the convolutional decoder has second weights.

Buffering streaming content includes accessing prior device location data of a device and predicting a future sector that the device will travel through based at least in part on the prior device location data. A predicted quality of service of wireless communications is determined and a streaming buffer is adjusted based at least in part on the predicted quality of service and a caching policy set in accordance with key variables related to network conditions in the future sector.

An infrastructure management subsystem receives a selection of a planned configuration of the computing infrastructure and a baseline demand that includes a current usage of computing resources of the computing infrastructure. The infrastructure management subsystem determines an anticipated turbulence. The anticipated turbulence includes a quantitative indication of anticipated fluctuations in future infrastructure demand as a function of time. The infrastructure management subsystem determines an effective turbulence for the planned infrastructure configuration. The effective turbulence includes a quantitative indication of anticipated fluctuations in future infrastructure availability. The infrastructure management subsystem determines a configuration score corresponding to an extent to which the anticipated fluctuations in the effective turbulence destructively interfere with the anticipated fluctuations in the anticipated turbulence.

An infrastructure management subsystem receives a selection of a planned configuration of the computing infrastructure and a baseline demand that includes a current usage of computing resources of the computing infrastructure. The infrastructure management subsystem determines an anticipated turbulence. The anticipated turbulence includes a quantitative indication of anticipated fluctuations in future infrastructure demand as a function of time. The infrastructure management subsystem determines an effective turbulence for the planned infrastructure configuration. The effective turbulence includes a quantitative indication of anticipated fluctuations in future infrastructure availability. The infrastructure management subsystem determines a configuration score corresponding to an extent to which the anticipated fluctuations in the effective turbulence destructively interfere with the anticipated fluctuations in the anticipated turbulence.