Various systems and methods for determining and communicating Link Performance Predictions (LPPs), such as in connection with management of radio communication links, are discussed herein. The LPPs are predictions of future network behaviors/metrics (e.g., bandwidth, latency, capacity, coverage holes, etc.). The LPPs are communicated to applications and/or network infrastructure, which allows the applications/infrastructure to make operational decisions for improved signaling/link resource utilization. In embodiments, the link performance analysis is divided into multiple layers that determine their own link performance metrics, which are then fused together to make an LPP. Each layer runs different algorithms, and provides respective results to an LPP layer/engine that fuses the results together to obtain the LPP. Other embodiments are described and/or claimed.

Systems, methods, and instrumentalities are disclosed for switching a control scheme to control a set of system modules and/or modular devices of a surgical hub. A surgical hub may determine a first control scheme that is configured to control a set of system modules and/or modular devices. The surgical hub may receive an input from one of the set of modules or a device located in an OR. The surgical hub may make a determination that at least one of a safety status level or an overload status level of the surgical hub is higher than its threshold value. Based on at least the received input and the determination, the surgical hub may determine a second control scheme to be used to control the set of system modules. The surgical hub may send a control program indicating the second control scheme to one or more system modules and/or modular devices.

Techniques are disclosed for generating a virtual representation (e.g., one or more digital twin models) of a multi-access edge computing system environment, and managing the multi-access edge computing system environment via the virtual representation. By way of example only, such techniques enable understanding, prediction and/or optimization of performance of applications and/or systems operating in the multi-access edge computing environment.

Systems and methods that adaptively model network traffic to predict network capacity utilization and quality of experience into the future. The adaptive model of network traffic may be used to recommend capacity upgrades based on a score expressed in a QoE space.

Systems and methods for forecasting time series data are provided. In one implementation, a method includes the steps of obtaining time series data from a network. The method also comprises the step of determining one or more forecasters to be used based on a type of the time series data and based on previous training that determine that the one or more forecasters from a number of forecasters are best suited for the type of time series data. The method further comprises making a forecast of the time series data using the one or more forecasters and to save and/or display the forecast.

Wireless communications and/or systems (e.g., 100) and/or methods (e.g., 200, 300, 400) may be provided for predicting of potential undetected flows in a DPI system using a machine learning (ML) model. The system may include an input packet module which may be configured for verifying packet parameters from a network traffic flow, and a processor which can be configured for processing the extracted parameters to identify whether the network traffic flow is potentially detectable or undetectable using a trained machine learning (ML) model based on at least the extracted parameters and perform DPI processing for the detectable flows. Thus, the system may provide an optimized DPI flow processing for high rate traffic networks with decreasing processing time.

Systems, methods, and computer-readable media for receiving one or more models of network intents, comprising a plurality of contracts between providers and consumers, each contract containing entries with priority values. Each contract is flattened into a listing of rules and a new priority value is calculated. The listing of rules encodes the implementation of the contract between the providers and the consumers. Each entry is iterated over and added to a listing of entries if it is not already present. For each rule, the one or more entries associated with the contract from which the rule was flattened are identified, and for each given entry a flat rule comprising the combination of the rule and the entry is generated, wherein a flattened priority is calculated based at least in part on the priority value of the given one of given entry and the priority value of the rule.

Techniques are described for monitoring application performance in a computer network. For example, a network management system (NMS) includes a memory storing path data received from a plurality of network devices, the path data reported by each network device of the plurality of network devices for one or more logical paths of a physical interface from the given network device over a wide area network (WAN). Additionally, the NMS may include processing circuitry in communication with the memory and configured to: determine, based on the path data, one or more application health assessments for one or more applications, wherein the one or more application health assessments are associated with one or more application time periods for a site, and in response to determining at least one failure state, output a notification including identification of a root cause of the at least one failure state.

Embodiments of the present disclosure provide for predicted operational support data object selection and provision functionality. Predicted operational support data object(s) may be selected and provided to address particular malfunction classification(s) affecting networked device(s) on a dynamic home communications network. Some embodiments include identifying, in real-time, a device identification data set associated with a networked device set communicable with the dynamic home communications network; retrieving a device activity data set associated with the networked device set; applying a malfunction classification data model to the device activity data set to select the predicted operational support data object from the device operational support management repository; and outputting the predicted operational support data object to a client device in communication with the dynamic home communications network. The malfunction classification data model is trained based on training data, external aggregated activity data, and malfunction device history data.

Systems and methods for updating a configuration of an SD-WAN application using connectivity or usage data include a software-defined wide area network (SD-WAN) application executing on a client device receiving one or more of historic or predictive data relating to connectivity or usage of the client device. The SD-WAN application detects an update condition for the SD-WAN application using the one or more historic or predictive data relating to the connectivity or usage of the client device. The SD-WAN application updates a configuration of the SD-WAN application responsive to detecting the update condition. The SD-WAN application transmits application traffic using the updated configuration.