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.

A method of reinforcement learning is used for placement of a plurality of service functions at nodes of a telecommunications network. The state of the system is defined by an allocation matrix, wherein each first vector of the allocation matrix corresponds to a respective one of the nodes of the telecommunications network, each second vector of the allocation matrix corresponds to a respective one of the plurality of service functions. Moreover, each cell of the allocation matrix contains a value 1 if the one of the plurality of service functions corresponding to the respective second vector is placed on the one of the nodes of the telecommunications network corresponding to the respective first vector, and otherwise contains a value 0.

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.

For providing an efficient network use and resource allocation within the network a method for operating a network is provided, wherein user network traffic is controlled by an operator, comprising the following steps: a) monitoring user network traffic data on a per user basis, b) using said network traffic data in a learning process for providing a prediction of user network traffic on a per user basis, and c) controlling user network traffic under consideration of said prediction, including allocating network resources under consideration of said prediction to one or more users, preferably for providing a definable Quality of Service, QoS, per at least ne of said one or more users and/or per at least one other user. Further, a corresponding network is claimed.

Novel tools and techniques are provided for implementing intent-based orchestration using network parsimony trees. In various embodiments, in response to receiving a request for network services that comprises desired characteristics and performance parameters for the requested network services without information regarding specific hardware, hardware type, location, or network, a computing system might generate a request-based parsimony tree based on the desired characteristics and performance parameters. The computing system might access, from a datastore, a plurality of network-based parsimony trees that are each generated based on measured network metrics, might compare the request-based parsimony tree with each of one or more network-based parsimony trees to determine a fitness score for each network-based parsimony tree, and might identify a best-fit network-based parsimony tree based on the fitness scores. The computing system might identify and might allocate network resources based on the identified best-fit network-based parsimony tree, for providing the requested network services.

Novel tools and techniques are provided for implementing intent-based orchestration using network parsimony trees. In various embodiments, in response to receiving a request for network services that comprises desired characteristics and performance parameters for the requested network services without information regarding specific hardware, hardware type, location, or network, a computing system might generate a request-based parsimony tree based on the desired characteristics and performance parameters. The computing system might access, from a datastore, a plurality of network-based parsimony trees that are each generated based on measured network metrics, might compare the request-based parsimony tree with each of one or more network-based parsimony trees to determine a fitness score for each network-based parsimony tree, and might identify a best-fit network-based parsimony tree based on the fitness scores. The computing system might identify and might allocate network resources based on the identified best-fit network-based parsimony tree, for providing the requested network services.

This application provides a method, device, and system for determining a bandwidth for service flow transmission. The method includes: A first device obtains a first traffic sampling set of a service flow, where the first traffic sampling set includes one or more pieces of traffic sampling information. The first device obtains a service level parameter corresponding to the service flow and a reliability probability of meeting the service level parameter. The first device determines, based on the first traffic sampling set, the service level parameter, and the reliability probability, a bandwidth for transmitting the service flow.

This application provides a method, device, and system for determining a bandwidth for service flow transmission. The method includes: A first device obtains a first traffic sampling set of a service flow, where the first traffic sampling set includes one or more pieces of traffic sampling information. The first device obtains a service level parameter corresponding to the service flow and a reliability probability of meeting the service level parameter. The first device determines, based on the first traffic sampling set, the service level parameter, and the reliability probability, a bandwidth for transmitting the service flow.

Systems and methods are described to enable a so-called ‘unified slice’, wherein the unified slice is technology-independent, i.e., constructed from different networking technologies, and spans multiple operators. The method provides an abstraction of a network slice and its segments, and a way to coordinate the end-to-end slice information collection, slice segment configuration and activation across multiple types of networks and operators. The system of invention has the task of coordinating configuration of an end-to-end slice, with user-specified slice parameters, by communicating with the respective slice managers; It receives information to generate an abstract model of each slice segment, and sends the required slice segment attributes to these slice managers so that they can activate the segment after translating them according to capabilities of their network technology.

Systems and methods are described to enable a so-called ‘unified slice’, wherein the unified slice is technology-independent, i.e., constructed from different networking technologies, and spans multiple operators. The method provides an abstraction of a network slice and its segments, and a way to coordinate the end-to-end slice information collection, slice segment configuration and activation across multiple types of networks and operators. The system of invention has the task of coordinating configuration of an end-to-end slice, with user-specified slice parameters, by communicating with the respective slice managers; It receives information to generate an abstract model of each slice segment, and sends the required slice segment attributes to these slice managers so that they can activate the segment after translating them according to capabilities of their network technology.