Cisco Technology, Inc.

Carlos M Pignataro

Nagendra Kumar Nainar

Rajiv Asati

Joseph Michael Clarke

James Neil Guichard

David Delano Ward

Gonzalo A Salgueiro

Paul M Quinn

Jerome Henry

Frank Brockners

David John Zacks

Shwetha Subray Bhandari

K Tirumaleswar Reddy

Pascal Thubert

Clarence Filsfils

Robert E Barton

Prashanth Patil

Jaganbabu Rajamanickam

Thomas Szigeti

Zafar Ali

Bart A Brinckman

Benoit Claise

Syed Kamran Raza

Eric Albin Voit

Stefano Benedetto Previdi

Robert Edgar Barton

Stewart Frederick Bryant

George Swallow

Hugo Latapie

Enzo Fenoglio

Akram Ismail Sheriff

Rakesh Gandhi

Ahmed Refaat Bashandy

Rajesh Kumar

David C White, Jr

Ijsbrand Wijnands

Timothy Peter Stammers

Keyur Patel

Antonio Nucci

Muthurajah Sivabalan

Om Prakash Suthar

Luca Muscariello

Jim Guichard

Reshad Rahman

Javed Asghar

Daniel C Frost

Srinath Gundavelli

Vimal Srivastava

Walter Theodore Hulick, Jr

Surendra M Kumar

Vinay Saini

Nitesh Trikha

Ram Mohan Ravindranath

Naiming Shen

Shankar Ramanathan

Michel Khouderchah

Chidambaram Arunachalam

Rajesh Indira Viswambharan

Mark Grayson

Charles Calvin Byers

Kent Kinchu Leung

Sami Boutros

Daniel George Wing

Kyle Andrew Donald Mestery

Aeneas Sean Dodd-Noble

Alvaro E Retana

Jay Kemper Johnston

Mankamana Prasad Mishra

Magnus Mortensen

Ian James Wells

John Matthew Swartz

Giovanna Carofiglio

Cedell Adam Alexander, Jr

Aamer Saeed Akhter

Plamen Nedeltchev

Jianxin Wang

Chandrasekar Krishnamurthy

Arghya T Mukherjee

John T Chapman

Vivek Agarwal

Nitin Kumar

Louis Gwyn Samuel

Yi Yang

Malcolm Muir Smith

Prakash C Jain

Alon Shlomo Bernstein

Domenico Ficara

Milind R Naphade

Tarek Saad

Peter Psenak

Rex Emmanuel Fernando

Amine Choukir

Pradosh Mohapatra

Salvatore Valenza

Ammar Rayes

L Alexander Clemm

Darren Russell Dukes

Vincent Cuissard

Enke Chen

Lionel Florit

Devices, systems, methods, and processes for managing network devices through generated predictions and associated confidence levels are described herein. Networks within a floorplan can be operated at full capacity all day in an inefficient way when not adjusted due to traffic patterns and seasonality changes. Data related to the topology of the network, along with historical data can be utilized to generate predictions of various network needs. For example, the overall network throughput capacity needs may be predicted for a series of points in the future. An associated confidence level can be generated as well including one or more confidence intervals. These can be utilized to select a future need for the network and generate a corresponding sustainable network configuration for the network devices and/or their transceivers that can provide sufficient network needs while minimizing the overall power used. This can be automated over time once trust has been established.

Client-aware floorplan management with predicted confidence levels

Techniques for determining an optimal connection path by a NHNaaS are described. The techniques may include receiving a registration from an IPS that includes service ISP service parameters, and storing the registration in a NaaS database. A request to connect to a remote service from a user device, including user parameters required is received. ISPs having respective service parameters compatible with the user parameters are identified in the NaaS database. Multiple paths offered by the service providers between the user device and the remote service are determined. Network performance data for each path is received from a network monitoring service. Using the network performance data, an optimal path for establishing the connection is identified. A request to instantiate a tunnel between the user device and remote service is transmitted to the service providers along the optimal path and the tunnel information is transmitted to the user device.

End-to-end neutral host network-as-a-service

A method, computer system, and computer program product are provided for decentralized machine learning. A plurality of computing networks are identified by determining that each computing network of the plurality of computing networks satisfies a predetermined number of criteria. A decentralized learning agent is provided to each computing network, wherein the decentralized learning agent is provided with input parameters for training and is trained using training data associated with a computing network to which the decentralized learning agent is provided. A plurality of learned parameters are obtained from the plurality of computing networks, wherein each learned parameter of the plurality of learned parameters is obtained by training the decentralized learning agent provided to each respective computing network. A global model is generated based on the plurality of learned parameters.

Decentralized machine learning across similar environments

A method is described that involves a method for application performance in a cross-domain network using multi-controller coordination. The method includes receiving a request at a first controller from an application within an enterprise network, wherein the request seeks to monitor a network path. The first controller identifies one or more additional controllers within the enterprise network capable of monitoring the network path. These additional controllers include a first one within the same domain as the first controller (first domain) and a second one within a different domain (second domain). A token, encompassing authentication details and monitoring requirements linked to the request, is sent to the second additional controller from the first domain. Subsequently, direct communication is established with a resource of the second controller, allowing monitoring of the request and its associated operation.

Conveying operator intent to remote agents in an administrative-constrained cross-domain multi-controller FSO network

Devices and methods that incorporate sustainability data within a header of a data packet to allow for the generation of sustainable configurations for various network devices are disclosed. Power efficiency is obtained at a node-level by including metadata to existing network flows, in an in-band/in-situ configuration. This information may be used for optimum flow placement. Received data packets may be formatted with sustainability data within a metadata shim. The received data packets are processed, and a sustainable configuration is generated for the one or more network devices. The generated sustainable configuration is transmitted to the one or more network devices to enable efficient and effective management of network devices by incorporating sustainability data into the data packets.

In-band sustainable packet management

A method of orchestrating one or more radio resources among various services executing within a container. The method includes obtaining, by an orchestration engine executing on a network device, a request, from a first service of a plurality of services, for use of a physical/hardware resource that connects a container running on the network device to a network. The request from the first service has a particular priority. The plurality of services execute within the container. The method further includes determining whether to connect the first service to the network via the physical/hardware resource based on the priority and an availability status of the physical/hardware resource and establishing, at a kernel level, a connection between the first service and the physical/hardware resource based on the determining.

Resource orchestration for multiple services

In one embodiment, a service chain data packet is instrumented as it is communicated among network nodes in a network providing service-level and/or networking operations visibility. The service chain data packet includes a particular header identifying a service group defining one or more service functions, and is a data packet and not a probe packet. A network node adds networking and/or service-layer operations data to the particular service chain data packet, such as, but not limited to, in the particular header. Such networking operations data includes a performance metric or attribute related to the transport of the particular service chain packet in the network. Such service-layer operations data includes a performance metric or attribute related to the service-level processing of the particular service chain data packet in the network.

Segment routing network signaling and packet processing

Described herein are devices, systems, methods, and processes for optimizing network traffic distribution across multiple paths in a manner that is energy-efficient and environmental sustainability-aware. This may be achieved by leveraging time-series analytics and capacity planning based on seasonalities. Data associated with the Layer 3 topology of the network can be collected. Bandwidth can be pre-reserved on an energy-aware traffic engineering tunnel. The time-series data can be used to build a capacity plan based on the seasonalities. Nodes may be clustered based on usage patterns and network utilization seasonality. The data can be used to make decisions about when and where to combine or shut down paths for energy efficiency, while maintaining optimal network performance. A hysteresis mechanism may be incorporated to avoid oscillation when changing active links. Power savings can be achieved by fully turning off or depowering certain network components when they are not needed.

Sustainable network-wide optimization service

Methods are provided in which a cloud portal, serving as an orchestrator device, obtains a request for diagnostics data to monitor performance of a cloud-based service executing in a provider cloud network and accessed by a client device in an enterprise network and obtains service information about a plurality of assets involved in providing the cloud-based service. The plurality of assets are managed by at least a first cloud-based management entity and a second cloud-based management entity. The methods further include collecting the diagnostics data about the plurality of assets based on the service information, aggregating the diagnostics data to form a multi-cloud diagnostics data, and determining the performance of the cloud-based service based on the multi-cloud diagnostics data.

Case deflection using visibility into multi-product clouds

A method is performed by a network controller that is configured to control routers configured to forward a multicast flow downstream from a first hop router that is a root of a multicast tree formed by the routers to last hop routers that terminate branches of the multicast tree, respectively. The method includes collecting operational configuration information from the routers and constructing a topological view of the multicast tree based on the operational configuration information; causing the routers to forward multicast probes downstream from the first hop router along all of the branches toward the last hop routers to trace the multicast tree; receiving, from particular ones of the last hop routers that received the multicast probes, indications that the multicast probes were received; and detecting failures in the multicast tree based on the indications and the topological view.

In-situ operations, administration, and maintenance extension to trace multicast tree and identify failure node

Techniques for initiator-based data-plane validation of segment routed, multiprotocol label switched (MPLS) networks are described herein. In examples, an initiating node may determine to validate data-plane connectivity associated with a network path of the MPLS network. The initiating node may store validation data in a local memory of the initiating node. In examples, the initiating node may send a probe message that includes a request for identification data associated with a terminating node. The terminating node may send a probe reply message that includes the identification data, as well as, in some examples, a code that instructs the initiating node to perform validation. In examples, the initiating node may use the validation data stored in memory to compare to the identification data received from the terminating node to validate data-plane connectivity. In some examples, the initiating node may indicate a positive or negative response after performing the validation.

Initiator-based data-plane validation for segment routed, multiprotocol label switched (MPLS) networks

This disclosure describes techniques for performing application-based tagging. An example method includes receiving, at a virtual socket, non-packetized data from an application and generating, by the virtual socket, a label based on the application. One or more data packets are generated by packetizing at least a portion of the non-packetized data. A header field of the one or more data packets includes a tag based on the label.

Application-based data labeling

The present disclosure is directed to BIER forwarding over varying BSL domains, the methods including the steps of receiving, at a border node, a packet comprising a BIER header having a BIER bit string with a first bit string length; reading an incoming label of the packet comprising instructions to split the BIER header into a plurality of smaller headers associated with a plurality of smaller bit strings; generating a set of split bit masks; performing a separate bitwise AND operation on each split bit mask and the BIER bit string to generate the plurality of smaller bit strings, each copied to a corresponding smaller header of the plurality of smaller headers; and performing a lookup for each of the plurality of smaller headers on a respective forwarding table to determine one or more egress routers to which to transmit the packet.

Efficient BIER forwarding over varying BSL domain using header stitching

Energy-aware configurations can be utilized to operate a network based on sustainability-related metrics. In many embodiments, a suitable device includes a processor, a memory commutatively coupled to the processor, a plurality of elements, a communication port, and an energy-aware topology logic configured to collect topology data from one or more network devices, wherein each of the one or more network devices include a plurality of elements. The energy-aware topology logic can receive power source data and power usage data related to plurality of elements and generate an element energy coefficient (EEC) for a plurality of elements. Subsequently, the energy-aware topology logic can also generate an energy-aware configuration for at least one of the one or more network devices, and then pass the generated energy-aware configuration to the at least one network device, wherein the energy-aware configuration is configured to steer traffic based on at least one sustainability-related metric.

Energy-aware traffic forwarding and loop avoidance

Methods are provided in which a network device hosts distinct network access resources that are managed by different entities. The method includes obtaining a request for partitioning one or more network resources of an on-premise network device for connecting one or more endpoints to a first network managed by a first entity. The on-premise network device connects one or more endpoints to a second network managed by a different entity. The method further involves partitioning, based on the request, the one or more network resources and connecting the one or more endpoints to the first network using the one or more network resources. The one or more network resources are managed by the first entity while at least one other network resource of the on-premise network device is managed by the different entity and is associated with connecting the one or more endpoints to the second network.

Multitenant network orchestration

Growing community of inventors

Cary, NC, United States of America

Carlos M Pignataro