Ciena Corporation

Nortel Networks Corporation

Northern Telecom Limited

Todd D Morris

Petar Djukic

Romualdas Armolavicius

David Jordan Krauss

Li Li

Alan Frank Graves

David Côté

Norman Allan Lyon

Maged E Beshai

Stephen Daniel Shew

Donald Russell Ellis

Stacy W Nichols

Emil Janulewicz

Nalin Mistry

Christopher Janz

Maryam Amiri

Kenneth Gary Hayward

Syed I Shah

Sid Ryan

Kun Qian

James P Carnes, Iii

Mitchell Howard Auster

David Miedema

Kaniz A Mahdi

Thomas Triplet

Paul Littlewood

David Anthony Fisher

Robert Kevin Tomkins

Blake Toplis

Norman Steven Traxler

Patrick Premont

Jie Jenny Qin

Paul Gosse

Dana Dennis

Systems and methods for detecting patterns in data from a time-series and for detecting outliers in network data in an unsupervised manner are provided. In one implementation, a method includes the steps of obtaining network data from a network to be monitored and creating a window from the obtained network data. The method also includes the step of detecting outliers of the obtained data with respect to the window using an unsupervised deep learning process (e.g., using a Generalized Adversarial Network (GAN) learning technique and/or a Bidirectional GAN (BiGAN) learning technique) for enabling the learning of a data distribution. The unsupervised process, for example, does not require manual intervention.

Unsupervised outlier detection in time-series data

Systems and methods include monitoring packets, by a network edge device, from one or more endpoint devices where the packets are destined for corresponding application services in a network; classifying the one or more endpoint devices based on the monitoring into a corresponding trust level of a plurality of trust levels; and, responsive to a first endpoint device of the one or more endpoint devices being untrusted, steering the packets from the first endpoint device into a restricted zone.

Network architecture providing device identification and redirection using whitelisting traffic classification

Systems, methods, and computer-readable medium for forecasting a time-series are provided. In one implementation, a method is configured to include a step of providing a time-series to a neural network including one or more branches for processing one or more portions of the time-series. In each of the one or more branches, the method includes separating the respective portion of the time-series into individual portions and applying each portion to a respective sub-branch of a plurality of sub-branches of the one or more branches. The method also includes generating forecasting coefficients for each output time point in each of the respective sub-branches and providing a forecast of the time-series based at least on the forecasting coefficients.

Forecasting time-series data in a network environment

Deep Neural Networks (DNNs) for forecasting future data are provided. In one embodiment, a non-transitory computer-readable medium is configured to store computer logic having instructions that, when executed, cause one or more processing devices to receive, at each of a plurality of Deep Neural Network (DNN) forecasters, an input corresponding to a time-series dataset of a plurality of input time-series datasets. The instructions further cause the one or more processing devices to produce, from each of the plurality of DNN forecasters, a forecast output and provide the forecast output from each of the plurality of DNN forecasters to a DNN mixer for combining the forecast outputs to produce one or more output time-series datasets.

Forecasting routines utilizing a mixer to combine deep neural network (DNN) forecasts of multi-variate time-series datasets

Systems and methods for detecting patterns in data from a time-series are provided. In one implementation, a method for pattern detection includes obtaining data in a time-series and creating one-dimensional or multi-dimensional windows from the time-series data. The one-dimensional or multi-dimensional windows are created either independently or jointly with the time-series. The method also includes training a deep neural network with the one-dimensional or multi-dimensional windows utilizing historical and/or simulated data to provide a neural network model. Also, the method includes processing ongoing data with the neural network model to detect one or more patterns of a particular category in the ongoing data, and localizing the one or more patterns in time.

Pattern detection in time-series data

A network edge device includes switching circuitry configured to switch traffic from one or more endpoint devices to corresponding application services over a network; and processing circuitry configured to monitor the traffic from the one or more endpoint devices, compare the monitored traffic to classify the one or more endpoint devices into a corresponding trust level of a plurality of trust levels, and route the traffic from each of the one or more endpoint devices based on its corresponding trust level. The network edge element is configured to provide network connectivity to the one or more endpoint devices.

Systems and methods for detecting patterns in data from a time-series are provided. According to some implementations, the systems and methods may use network topology information combined with object recognition techniques to detect patterns. One embodiment of a method includes the steps of obtaining information defining a topology of a multi-layer network having a plurality of Network Elements (NEs) and a plurality of links interconnecting the NEs and receiving Performance Monitoring (PM) metrics and one or more alarms from the multi-layer network. Based on the information defining the topology, the PM metrics, and the one or more alarms, the method also includes the step of utilizing a Machine Learning (ML) process to identify a problematic component from the plurality of NEs and links and to identify a root cause associated with the problematic component.

Identifying and locating a root cause of issues in a network having a known topology

Systems and methods of service enhancement in a Software Defined Networking (SDN) network include performing an evaluation of one or more services in the SDN network for service enhancements; performing a scoring of the service enhancements of the one or more services; and causing implementation of at least one of the service enhancements in the SDN network. The evaluation can be based on temporarily implementing the service enhancements and measuring a benefit thereof. The evaluation can also be based on estimating the service enhancements based on historical measurements from the SDN network.

Service enhancement discovery for connectivity traits and virtual network functions in network services

System and methods for autonomous resource partitioning in a network include a resource controller configured to provision resources which are any of virtual resources and physical resources in one or more layers in the network and monitor availability of the resources in the network; a resource manager configured to determine the any of virtual resources and physical resources as required for Quality of Service (QoS) in the network; a resource broker configured to advertise and assign resource requests to corresponding resources; and a partition manager configured to track the utilization of the resources provided by the one or more layers and to adjust resource usage of the resources in negotiation with the resource broker to minimize a cost of implementation.

Autonomic resource partitions for adaptive networks

An optimization platform system includes a network interface configured to communicate with a plurality user devices and a plurality of servers in a network; a processor communicatively coupled to the network interface; and memory storing instructions that, when executed, cause the processor to obtain network measurements including Quality of Service (QoS) measurements and one of measured Quality of Experience (QoE) measurements and inferred QoE measurements from the QoS measurements for one or more streams in the network, wherein each of the one or more streams has a type selected from a group consisting of a video stream, a Voice over Internet Protocol (VoIP) call, a gaming stream, and an Augmented Reality (AR)/Virtual Reality (VR) stream, and wherein the QoE measurements and the inferred QoE measurements of each of the one or more streams is based on the type of the respective stream, analyze the QoE measurements to determine poor QoE in the network, determine remedial actions in the network to repair the poor QoE, and cause implementation in the network.

Adaptive systems and methods enhancing service quality of experience

Systems and methods for instantiating a network service in a network include receiving a service request for a network service; determining network resources in the network to instantiate the network service; determining a price for the network resources utilizing forecasts with adjustments to the price based on a current network status; providing the price in response to the service request; and responsive to acceptance of the price, causing instantiation of the network service through the network resources in the network.

Network service pricing and resource management in a software defined networking environment

Traffic-adaptive network control systems and methods for a network, implemented by a server, include monitoring data associated with the network; generating a traffic forecast based on the monitored data; generating a schedule of actions based on a comparison of the traffic forecast to observed data; and causing orchestration of the actions in the network based on the generated schedule. The network can include a Software Defined Networking (SDN) network.

Traffic-adaptive network control systems and methods

A method for path computation in a network includes determining dynamic link weights for links in the network, responsive to a request for a path, for a connection, between a source node and a destination node in the network with a requested bandwidth amount, wherein the dynamic link weights, for each link, are based on a current status of the link and a future status of the link; determining one or more paths for the request based on the dynamic link weights; and selecting a path of the one or more paths to minimize cost in the network. The method can be implemented through a Software Defined Networking (SDN) controller.

Optical and packet path computation and selection systems and methods

Modem reclamation systems and methods for optimizing optical modem use in a network include determining costs and capacity range for Ladjacencies in the network based on Linformation and Linformation, wherein Lis a digital layer with routed traffic and L0 is a media layer with optical modems that are reclaimed when their utilization is reduced in the digital layer; determining an order of Lconnection moves that minimizes the costs of the Ladjacencies, to reclaim or minimize the optical modem use; and performing the Lconnection moves based on the order and updating the Ladjacencies.

Multi-layer modem reclamation systems and methods

Systems and methods for a representative bandwidth calculation in a network include receiving a request for representative bandwidth information; obtaining network measurements; combining, filtering, and retiming network measurements based on the request to determine the representative bandwidth information. The systems and methods convert the combined, filtered, and retimed network measurements into the representative bandwidth information which has an additive property in an algebraic domain. The additive property enables the representative bandwidth information to be used for admission control, overbooking determinations, network planning, and/or dynamic pricing in the network.

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Stittsville, Canada

Todd D Morris