Cisco Technology, Inc.

Ammar Rayes

Aeneas Sean Dodd-Noble

Om Prakash Suthar

Ian McDowell Campbell

Michael David Geller

Subrata Dasgupta

Samer M Salam

Ajay Madhavan

James McDonnell

Samhith Venkatesh

Samer Salam

Shyyunn Lin

Sanjeev Ukhalkar

Michael Cheung

Virendra K Malaviya

Ali Mehregani

Silviu Marian Jingoi

Pradeep K Banerjee

Balaji R Venkatraman

Erin Lynne Brown

Gregory S Thompson

Anil Vasireddy

David F Hinnant

Carlos M Pignataro

Gonzalo A Salgueiro

Gargi Adhav

Hazim Hashim Dahir

David Prantl

Jiabin Zhao

Arun Mammen Thomas

Gaurav Garg

Rajesh Vargheese

James D Stanley, Iii

Michael Michaelides

Madhavan Kasthurirangan

Ram Prasanna Govind Krishnan

Frank L O Palmans

Vikrant Dev Andotra

Mark Whitteker

Tom Deckers

Venkataraman Sivasankaran

Asad Ghayas Ahmed

Zhiyun Qian

Thuan Quang Truong

Xixi Luo

Arabinda Samantaray

Mehiar Dabbagh

Described herein are devices and methods for calculating a network sustainability index (NSI) and providing specific remediation/action plans to achieve better sustainability in network infrastructures. The NSI is calculated based on one or more of three main parameters: a reporting parameter, an optimization parameter, and/or a device energy efficiency rating. The reporting parameter may evaluate the capability of each device to report its power consumption, considering factors such as granularity, component-level reporting, and reporting frequency. The optimization parameter may assess whether a device participates in network energy optimization. The device energy efficiency rating may evaluate the energy efficiency of each device. A recommendation portal offers actionable insights and remediation plans based on the calculated NSI. The devices and methods allow administrators to list eligible devices for energy optimization, ensuring a comprehensive approach to network sustainability and facilitating targeted measures for improved energy efficiency.

Network sustainability index

The present technology addresses a need in the art for an automated tool that allows users to create network-based custom workflows for networks and associated management applications. The users do not need to have in-depth network knowledge to work with the tool or even write any code/script. The tool provides the users with a flexible graphical user interface for automated troubleshooting, network provisioning, and closed-loop automation. Further, the tool uses a domain-independent semantic machine reasoning engine as an underlying engine and a mock data engine to test and validate network-based workflows created by the users.

Advanced workflow based self-serve automation system

Presented herein are methodologies for implementing a system and apparatus to estimate a network disruption index and undertake a mitigation action accordingly. A method includes calculating a network disruption index based on at least a disruption score associated with a service request measure, an end-of-life measure, a security incident response measure and a return material authorization measure for respective hardware devices in a network, comparing the network disruption index to a predetermined threshold, and when the network disruption index is above the predetermined threshold, identifying one or more of the hardware devices in the network for a mitigation action and implementing the mitigation action.

System and method to estimate network disruption index

The present technology addresses a need in the art for a closed-loop automation system of a managed network that detects and fixes an issue in the managed network with minimal to no human intervention. The closed-loop automation system employs a domain-independent semantic machine reasoning engine as an underlying engine. After detecting the issue in the managed network, the system searches through a knowledge base to find a match for the detected issue. The knowledge base includes previously collected knowledge from subject matter experts and maintenance teams. The system then proposes a remedial action to a user of the managed network for approval. After approval by the user, the system implements the remedial action.

Closed-loop automation of a managed network

A network function (NF) entity in a communication network receives authentication data associated with a User Equipment (UE), determines the UE supports a blockchain registration procedure based on the authentication data, exchanges authentication messages with a Blockchain Roaming Broker (BRB) entity over a blockchain network interface, receives a blockchain authentication confirmation from the BRB entity, and registers the UE with the core network based on the blockchain authentication confirmation.

Securing communications for roaming user equipment (UE) using a native blockchain platform

A Network Function (NF) entity in a telecommunication network receives blockchain credentials associated with UE and selects a Blockchain Charging Function (BCF). The NF entity further generates a Charging Data Record (CDR) corresponding to network resources, and sends a charging request based on the CDR (and policy rules) to the BCF entity over a blockchain network interface. The BCF entity sends a confirmation of the charging request, and the NF entity, based on the confirmation, provisions the network resources to the UE.

Provisioning network resources in a wireless network using a native blockchain platform

A network function (NF) entity in a communication network determines a User Equipment (UE) supports a blockchain authentication procedure, exchanges authentication messages with a Blockchain Authentication Function (BAF) entity over a blockchain network interface (e.g., based on the blockchain authentication procedure), receives a blockchain authentication confirmation from the BAF entity, and registers the UE based on the blockchain authentication confirmation.

Systems, devices, and techniques for registering user equipment (UE) in wireless networks using a native blockchain platform

A network function (NF) entity in a communication network receives session request data associated with a User Equipment (UE), which includes blockchain authentication data. The NF entity selects a Blockchain Authentication Function (BAF) entity based on the session request data, and exchanges at least a portion of the blockchain authentication data with the BAF entity over a blockchain network interface. The NF entity further receives authentication confirmation data from the BAF entity over the blockchain network interface, and establishes a data session associated with the UE based on the authentication confirmation data.

Systems, devices, and techniques for managing data sessions in a wireless network using a native blockchain platform

A plurality of data sets are compiled in a memory. A first data set comprises a location mapping of an item of interest within a predetermined environment, and a second set comprises energy usage data within the predetermined environment. The first data set is correlated, via a processor, with the second data set to locate a subset of energy usage data in the second data set associated with the item of interest. A third data set is generated by combining the first data set with the subset of energy usage data in the second data set to increase an accuracy of the location mapping of the item of interest based upon the subset of energy usage data. The third data set is applied to a computing resource.

Time and motion data fusion for high fidelity data sets

A network slice manager receives a workload mobility request to add network resources to a domain in the communication network, and authenticates a virtual network function (VNF) with a blockchain authentication function (BAF) over a blockchain network interface based on the workload mobility request. The network slice manager further receives an indication of a successful authentication from the BAF, and instantiates the VNF in the domain of the communication network based on the indication of the successful authentication. Notably, these authentication processes may be readily adapted to instantiate new network resources or migrate existing network resources between domains.

Native blockchain platform for improving workload mobility in telecommunication networks

Techniques are provided for predictively activating physical servers. Embodiments determine a plurality of physical servers that are available within one or more data centers. A plurality of virtual workload deployment requests that were processed within the one or more data centers during a historical window of time is determined. Embodiments classify each of the plurality of virtual workload deployment requests into one of a plurality of categories. A respective neural network prediction model is generated for each of the plurality of categories, based on the virtual workload deployment requests classified into the respective category. Embodiments then determine a number of physical servers to have active at a future moment in time, using the generated neural network prediction models.

Enhanced cloud demand prediction for smart data centers

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San Ramon, CA, United States of America

Ammar Rayes