A system may include a historical time series data store that contains electronic records associated with Software-as-a-Service (“SaaS”) applications in a multi-tenant cloud computing environment (including time series data representing execution of the SaaS applications). A monitoring platform may retrieve time series data for the monitored SaaS application from the historical time series data store and create tenant vector representations associated with the retrieved time series data. The monitoring platform may then provide the retrieved time series data and tenant vector representations together as final input vectors to an autoencoder to produce an output including at least one of a tenant-specific loss reconstruction and tenant-specific thresholds for the monitored SaaS application. The monitoring platform may utilize the output of the autoencoder to automatically detect an anomaly associated with the monitored SaaS application.

Network management apparatus and methods are described. A network management apparatus comprises network data receiving means for receiving network data that is representative of the current condition of a communications network, the network data comprising a plurality of values indicative of the performance of network elements; network data transformation means for transforming the received network data into a network state vector that is indicative of a current state of the network; and network state prediction means for predicting a future network state vector of the network from the current network state vector, the network state prediction means comprising a self-learning prediction module having a memory for storing at least one internal state.

Network management apparatus and methods are described. A network management apparatus comprises network data receiving means for receiving network data that is representative of the current condition of a communications network, the network data comprising a plurality of values indicative of the performance of network elements; network data transformation means for transforming the received network data into a network state vector that is indicative of a current state of the network; and network state prediction means for predicting a future network state vector of the network from the current network state vector, the network state prediction means comprising a self-learning prediction module having a memory for storing at least one internal state.

A method performed by a network computing device in a telecommunications network for adaptively deploying an aggregated machine learning model and an output parameter in the telecommunications network to control an operation in the telecommunications network. The network computing device can aggregate client machine learning models and an output performance metric the client machine learning models to obtain an aggregated machine learning model and an aggregated output performance metric. The network computing device can train a network machine learning model with the aggregated output performance metric and at least one measurement of a network parameter to obtain an output parameter. The network computing device can send to the client computing devices the aggregated machine learning model and the output parameter of the network machine learning model. A method performed by a client computing device is also provided.

A method performed by a network computing device in a telecommunications network for adaptively deploying an aggregated machine learning model and an output parameter in the telecommunications network to control an operation in the telecommunications network. The network computing device can aggregate client machine learning models and an output performance metric the client machine learning models to obtain an aggregated machine learning model and an aggregated output performance metric. The network computing device can train a network machine learning model with the aggregated output performance metric and at least one measurement of a network parameter to obtain an output parameter. The network computing device can send to the client computing devices the aggregated machine learning model and the output parameter of the network machine learning model. A method performed by a client computing device is also provided.

A network entity for a mobile telecommunications system, including circuitry configured to perform transmission rate control of data transmissions according to a transmission control protocol, wherein the transmission rate control is performed based on an output of a machine learning algorithm including a prediction of a congestion of the data transmissions.

A network entity for a mobile telecommunications system, including circuitry configured to perform transmission rate control of data transmissions according to a transmission control protocol, wherein the transmission rate control is performed based on an output of a machine learning algorithm including a prediction of a congestion of the data transmissions.

A ratio of prediction liable to result in user requirement violation is reduced by adjusting results of resource design even if it is highly likely that the user requirement violation will incur a heavy penalty. There is provided a requirement specifying functional unit (11) that specifies a user requirement for a service of interest, and a resource design unit (12) that predicts, by machine learning, performance achievable at a plurality of resource settings in performing the service of interest and selects a resource setting that satisfies the specified user requirement, based on results of the prediction, wherein the resource design unit (12) generates a P model as a model for use to predict performance, the P model using a P-mode loss function obtained by adding a function to an N model that uses an existing N-mode loss function, the added function taking a finite value when actual performance is lower than predicted performance.

A network device for maintaining a communication network is provided. The network device includes a transceiver configured for operable communication with at least one device. The network device also includes a processor including a memory configured to store computer-executable instructions. When executed by the processor the instructions cause the network device to store a plurality of network capabilities associated with a plurality of connectivity categories and a plurality of network settings, receive, from a first device, a connectivity advertisement including at least one connectivity category for the first device, retrieve a subset of the plurality of network capabilities based on the at least one connectivity category, determine one or more network settings for the network device based on the at least one connectivity category for the first device and the subset of network capabilities, and implement the one or more network setting on the network device.

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.