The subject matter described herein provides systems and techniques for a network planning and optimization tool that may allow for network capacity planning using key network failures for an arbitrary pair of network topology and demands. Performing network capacity planning with key network failures, instead of using other techniques, may avoid over-building the topology of a network. In particular, key network failures may be generated from the probabilistic failures, and the impact of these failures on a network may be computed. Expected flow availability SLO or a function thereof may be computed, using this information, and used by the tool to design a robust network. With an embedded flow availability calculation and updated risk framework, the capacitated cross-layer network topologies output by the tool may meet network demands/flows with their respective SLO type at the lowest cost.

Systems and methods are disclosed to implement a time series anomaly detection system that uses configurable statistical control rules (SCRs) and a forecasting system to detect anomalies in a time series data (e.g. fluctuating values of a network activity metric). In embodiments, the system forecasts future values of the time series data along with a confidence interval based on seasonality characteristics of the data. The time series data is monitored for anomalies by comparing actual observed values in the time series with the predicted values and confidence intervals, according to the SCRs. The SCRs may be defined and tuned via a configuration interface that allows users to visually see how different SCRs perform over real data. Advantageously, the disclosed system allows users to create custom anomaly detection triggers for different types of time series data, without use of a monolithic detection model which can be difficult to tune.

Systems and methods are disclosed to implement a time series anomaly detection system that uses configurable statistical control rules (SCRs) and a forecasting system to detect anomalies in a time series data (e.g. fluctuating values of a network activity metric). In embodiments, the system forecasts future values of the time series data along with a confidence interval based on seasonality characteristics of the data. The time series data is monitored for anomalies by comparing actual observed values in the time series with the predicted values and confidence intervals, according to the SCRs. The SCRs may be defined and tuned via a configuration interface that allows users to visually see how different SCRs perform over real data. Advantageously, the disclosed system allows users to create custom anomaly detection triggers for different types of time series data, without use of a monolithic detection model which can be difficult to tune.

UE capability for support of machine-learning (ML) based channel environment classification may be reported by a user equipment to a base station, where the channel environment classification classifies a channel environment of a channel between the UE and a base station based on one or more of UE speed or Doppler spread, UE trajectory, frequency selectivity or delay spread, coherence bandwidth, coherence time, radio resource management (RRM) metrics, block error rate, throughput, or UE acceleration. The user equipment may receive configuration for ML based channel environment classification, including at least enabling/disabling of ML based channel environment classification. When ML based channel environment classification is enabled, UE assistance information for ML based channel environment classification, and/or an indication of the channel environment (which may be a pre-defined channel environment associated with a lookup table), may be transmitted by the user equipment to the base station.

A redundant communication apparatus includes a determining unit and a transmitting unit. The determining unit determines an upper limit of a redundancy level when transmission waiting data in a transmission apparatus is made redundant and transmitted, based on a data quantity of the transmission waiting data and a communication speed of a communication line between a reception apparatus that is a transmission destination of the transmission waiting data and the transmission apparatus. The transmitting unit causes the transmission waiting data to be made redundant at a redundancy level that is equal to or less than the upper limit determined by the determining unit and transmits the data to the reception apparatus via the communication line.

A redundant communication apparatus includes a determining unit and a transmitting unit. The determining unit determines an upper limit of a redundancy level when transmission waiting data in a transmission apparatus is made redundant and transmitted, based on a data quantity of the transmission waiting data and a communication speed of a communication line between a reception apparatus that is a transmission destination of the transmission waiting data and the transmission apparatus. The transmitting unit causes the transmission waiting data to be made redundant at a redundancy level that is equal to or less than the upper limit determined by the determining unit and transmits the data to the reception apparatus via the communication line.

In some examples, a method includes monitoring device management actions. In some examples, the method includes predicting whether a proposed operation will trigger a device management action based on triggers and the device management actions. In some examples, the method may include generating a message in response to predicting that the proposed operation will trigger the device management action.

In one aspect, a device may include a processor and storage accessible to the processor. The storage may include instructions executable by the processor to determine an insufficiency related to a system in a first instance based on input from an end user. The instructions may also be executable to analyze first data related to the first instance and, based on the analysis, determine that the insufficiency has or will occur again based on second data also related to the system but that corresponds to a second instance occurring after the first instance. The instructions may then be executable to proactively address the insufficiency based on determination that the insufficiency has or will occur again. In some examples, the determination that the insufficiency has/will occur again may be performed using an artificial neural network trained using the first data to infer whether the insufficiency has or will occur again.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured accurately and programmatically train a responder prediction machine learning model for generating response team predictions based on the systematic collection of one or more responder prediction training corpuses comprising one or more alert related datasets in a responder prediction server system. For example, the responder prediction server system may extract one or more alert attributes for each of the one or more alert related datasets for training one or more responder prediction machine learning models and/or one or more prioritization machine learning models. The responder prediction machine learning model and prioritization machine learning models may process one or more alerts, in real-time, to generate one or more response team prediction objects for rendering in a response team suggestion interface.

The present disclosure is generally related to edge computing technologies (ECTs), communications networking, network slicing, and in particular, to techniques and technologies for providing flow-specific network slices. In particular, the present disclosure describes mechanisms that expand existing end-to-end architectures in order to include quality of service and monitoring mechanisms that connect network slicing technologies with infrastructure and/or network data center quality of service provider domains. The described mechanisms provide data center bridging to enable network, edge computing, and cloud computing domains.