Ciena Corporation

Paul Hudgins

Aung Htay

Mallikarjunappa Kore

Marek Gimza

Hari Pabbati

S Kris Chou

Systems and methods include receiving visual representations of components in network elements in a network; responsive to training a machine learning model to evaluate the visual representations, analyzing the visual representations using the machine learning model to determine a status of the components; and providing the status of the components to an inventory application for any of updating the inventory application and reconciling existing data in the inventory application.

Utilizing images to evaluate the status of a network system

Systems and methods for creating a license during a purchasing process between a customer and a vendor are provided. In one implementation, a method may be executable by a vendor system in communication with a customer device in a decentralized computing infrastructure. The method may include the steps of receiving a purchase order from a customer to initiate a purchase of a software or hardware product, where the customer is associated with the customer device, and receiving a public key from the customer. Additionally, the method may include the steps of creating a smart contract for controlling a license related to the purchase of the software or hardware product and sending a contract address associated with the smart contract to the customer to enable the customer to activate the software or hardware product.

Systems and methods for creating and activating a license

Methods for evaluating a system to determine the status of the system are provided in the present disclosure. In one implementation, a method may be configured to include a step of obtaining one or more visual representations of one or more components of a network system. Responsive to training a Machine Learning (ML) model to evaluate the one or more components of the network system, the method may also include a step of analyzing the one or more visual representations using the ML model for evaluating a status of the one or more components of the network system.

A method, a Software-Defined Networking (SDN) controller, and a network include operation of a multi-layer SDN network and uniquely identifying streaming content on higher layers relative to the multi-layer SDN network through deep packet inspection; associating the streaming content to a multi-layer service on the SDN network; and monitoring the streaming content on the SDN network over the multi-layer service. This can include dynamically adjusting bandwidth of the multi-layer service utilizing OpenFlow on the SDN network based on the monitoring. The deep packet inspection can utilize a Bloom filter embedded in a resource identifier of the streaming content by the content provider, wherein the embedded Bloom filter is transparent to content players and does not require changes to storage on associated web servers for the streaming content.

Dynamic bandwidth allocation systems and methods using content identification in a software-defined networking controlled multi-layer network

A computer-implemented method, a controller, and a Software Defined Network (SDN) perform steps of correlating users based on unique identifiers to service addresses; receiving historical data from associated control area operator for the service addresses; correlating the users' bandwidth usage behavior to the users' power usage behavior; monitoring the users' bandwidth usage over time; characterizing bandwidth usage type for the users with abnormal bandwidth usage patterns; and notifying the control area operator of the users with the abnormal bandwidth usage patterns based on the characterized bandwidth usage type. Accordingly, electric power forecasting by the control area operator can include improved accuracy through correlating the users' bandwidth usage behavior and detecting abnormal conditions.

Bandwidth analytics in a software defined network (SDN) controlled multi-layer network for dynamic estimation of power consumption

A method, through a server, to determine carbon footprint of a network service, in a network, includes determining power consumption and cooling requirements of network devices associated with the network service; determining associated carbon footprint coefficients for power sources associated with each of the network devices; and determining carbon footprint of the network service based on the power consumption, the cooling requirements, and the associated carbon footprint coefficients.

Systems and methods to compute carbon footprint of network services with network function virtualization (NFV) and software defined networking (SDN)

A method, a server, and a management system correlate performance data in loosely coupled software, such as network and element management systems, with minimal overhead. The systems and methods can be used to determine performance bottlenecks and interdependencies between components by correlating and analyzing collected data. In an exemplary embodiment, the systems and methods can relate to network management systems (NMSs), element management systems (EMSs), and the like, which are collectively referred to herein as management systems. Management systems can include a loosely coupled software architecture that utilizes a message-based communication mechanism. As messages are processed, synchronous calls between components of the management system as employed as well. For the purpose of performance analysis and optimization, the systems and method correlate data between synchronous and asynchronous interactions as each message is processed through a management system.

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Roswell, GA, United States of America

Paul Hudgins