A machine-learning driven Database Management System (DBMS) is provided. One or more machine-learning algorithms are trained on the database constructs and execution plans produced by a database optimizer for queries. The trained machine-learning algorithms provide predictors when supplied the constructs and plans for a given query. The predictors are processed by the DBMS to make resource, scheduling, and Service Level Agreement (SLA) compliance decisions with respect to the given query.

In one embodiment, a device obtains application experience metrics for an online application. The device predicts, based on the application experience metrics, future application experience metrics for each of a set of provider endpoints for the online application. The device selects, based on the future application experience metrics, a particular provider endpoint from among the set of provider endpoints. The device provides, to a Domain Name System (DNS) resolver, resolution information for one or more of the set of provider endpoints that causes a query for one of those provider endpoints to resolve to an address of the particular provider endpoint.

In one embodiment, a device obtains application experience metrics for an online application. The device predicts, based on the application experience metrics, future application experience metrics for each of a set of provider endpoints for the online application. The device selects, based on the future application experience metrics, a particular provider endpoint from among the set of provider endpoints. The device provides, to a Domain Name System (DNS) resolver, resolution information for one or more of the set of provider endpoints that causes a query for one of those provider endpoints to resolve to an address of the particular provider endpoint.

A failure effects estimating device includes an input unit that inputs a log and a traffic amount obtained from a communication system when an abnormality occurs, an estimating unit that estimates a failure effects amount in the communication system, on the basis of the log and the traffic amount, and an output unit that outputs the failure effects amount estimated by the estimating unit.

Described are examples for providing service level monitoring for a network hosting applications as a cloud service. A service level monitoring device may receive end-to-end measurements of service usage collected at user devices for a plurality of applications hosted as a cloud services. The service level monitoring device may determine degraded applications of the plurality of applications based on anomalies in the measurements. The service level monitoring device may determine a service level metric based on an aggregation of the degraded applications. In some examples, the service level monitoring device may detect a network outage affecting the service.

Disclosed are systems and methods for a robust Self-Organizing Network (SON) framework that quantifies SON applications' control and management of a network into key performance indicators (KPI) that are leveraged to determine the impact of a SON's application effectiveness in regulating network parameters, which then dictates how the SON application operates. The disclosed framework is configured to receive multiple data streams from existing data sources, determine the performance of a node on a network, and then automatically perform SON operations based therefrom. The disclosed framework can utilize this information to predict additional and/or future opportunities for SON automation on the network.

One example method includes assessing remote working environments. Conditions and characteristics or remote working environments are incorporated into service level agreements as indicators and objectives. This allows an entity to ensure that remote working environments meet certain standards.

A network device includes: a network interface configured to receive target data, a first processor configured to determine feature information of the target data, a second processor configured to perform preprocessing on the feature information, and a third processor configured to perform inference on a preprocessing result. In addition, the second processor is further configured to perform policy analysis based on an inference result.

The disclosure relates to a method, executed by an NFV-MANO, for providing a requested Service Availability Level (SAL) for a Network Service (NS). The method comprises at each of a plurality of layers of the NFV-MANO, mapping the requested SAL to a SAL that needs to be provided by a lower layer of the NFV-MANO. The method comprises propagating the mapped requested SAL through interfaces between layers of the NFV-MANO, from an NFVO towards a VIM. The method comprises receiving an estimated SAL′ for the NS based on virtual resources (VR) allocated by the VIM for satisfying the requested SAL. The method comprises, upon determining that the estimated SAL′ does not satisfy the requested SAL, taking actions to meet the requested SAL, or upon determining that the estimated SAL′ satisfies the requested SAL taking no further actions.

The disclosure relates to a method, executed by an NFV-MANO, for providing a requested Service Availability Level (SAL) for a Network Service (NS). The method comprises at each of a plurality of layers of the NFV-MANO, mapping the requested SAL to a SAL that needs to be provided by a lower layer of the NFV-MANO. The method comprises propagating the mapped requested SAL through interfaces between layers of the NFV-MANO, from an NFVO towards a VIM. The method comprises receiving an estimated SAL′ for the NS based on virtual resources (VR) allocated by the VIM for satisfying the requested SAL. The method comprises, upon determining that the estimated SAL′ does not satisfy the requested SAL, taking actions to meet the requested SAL, or upon determining that the estimated SAL′ satisfies the requested SAL taking no further actions.