At&t Intellectual Property I, LP

Shopify Inc.

At+t Corp.

David H Lu

Paritosh Bajpay

Chen-Yui Yang

Anthony Mark Srdar

Xidong Wu

Eshrat Huda

Hossein Eslambolchi

John McCanuel

Chaoxin Charles Qiu

Shiu Y Chong

Gabriel Bourge

Moshiur Rahman

Wen-Jui Li

Carolyn Bekampis

Balaji R Puli

Chee Ching

Zhijian Xu

Sam Russell

Scott Bradley Baker

Monowar Hossain

Anand Kumar Singh

Luis Figueroa

Rajan Anand

Sachin Lohe

Mihail Vasilescu

Nimish Buch

Timothy Neville

Mark Cottrell

John Soares

Raghvendra Savoor

Leah L Zhang

Kevin L D'Souza

Thiru Ilango

Robert Best, Jr

Fang Wu

Robert J Ferro

Joseph Lu

Jin Wang

Michael John Zinnikas

Zhiqiang Qian

Roberta Ann Bienfait

Jeffrey Stein

Olivier Daude

Li-Jin Wu Chung

Richard Alan Farel

Xiaochuan Yi

Chuan-Chuen Chang

John H Mayr

Mark Hunt

Russell Fischer

Nino Girardi

Michael R Lambert

Prafulla Verma

Paul Kahn

Chen Yui Yang

Sarma Tanuku

Mark Rogaski

Mohammed Shariff

Thomas Dickey

Zhi Li

Harold Jeffrey Stewart

Mohammad Nikain

Charles Giddens

Daniel Connolly

Madhukar M Kshirsagar

Jiyuan Wang

Min-Hsuan Chen

Kenneth A Williams

Ajay Joshi

Minghsien Wang

Quangchung Yeh

Muhammad Azam

Angelo Estela

Li-Jun W Chung

Peter P Bleznyk

John Steven Engelmann

Kenneth James Smith

John Vincent Wilson

Muralidhar Siddabathula

Sreelatha Bandla

Facilitating radio access network on-demand dynamic bandwidth allocation in advanced networks is provided herein. Operations of a system include determining that a group of network slice resources associated with network equipment fail to satisfy a specification applicable to a user equipment. The operations can also include extracting a first group of resource blocks from a first network slice resource of the group of network slice resources. Further, the operations can include reallocating the first group of resource blocks to a second network slice resource of the group of network slice resources. A combination of the first group of resource blocks and a second group of resource blocks of the second network slice resource is determined to satisfy the specification applicable to the user equipment.

Facilitating radio access network on-demand dynamic bandwidth allocation in advanced networks

An automated system provides end-to-end (E2E) orchestration for machine learning (ML)-enabled and software-defined network (SDN)-enabled reactive and predictive (e.g., 5G) cell-on-drone (COD) dispatch and deployment. The system may be configured to detect or predict traffic surges (e.g., relative to typical or baseline levels) in a given cell site and orchestrate provisioning of resources, including COD deployment, to address the surge. COD deployment may involve one or more clusters or colonies of drones equipped with low-powered cellular radio access (or small cell) nodes, where each cluster includes (or is led by) one or more SDN-equipped drones that provide intelligent SDN-on-drone (SOD) functionality.

Universal uncrewed aerial vehicle (UAV) colony wireline wireless orchestration and automation management

In one example, the present disclosure describes a device, computer-readable medium, and method for proactively adjusting the infrastructure of a communications network in response to reporting of real-time network performance. For instance, in one example, a method includes obtaining real-time network performance metrics directly from a user endpoint device operated by a customer of a telecommunication service provider network, correlating the real-time network performance metrics with data from another data source, wherein the data includes data other than network performance metrics, and adjusting an infrastructure of the telecommunication service provider network in response to an insight gleaned through the correlating.

Proactively adjusting network infrastructure in response to reporting of real-time network performance

Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.

Intelligent machine learning (ML)-enabled end-to-end (E2E) automated orchestration for collaborative next-generation wireless wireline convergence (WWC)

Concepts and technologies are disclosed related to allocation of hardware resources for virtual network function (VNF) deployment at sites based on templates. A design template defines hardware resources to allocate for a service, and a deployment policy defines deployment of the virtual network functions (VNFs) associated with the service to the hardware resources. Hardware resources are allocated at one or more sites based on the design template to host VNFs for the service, and VNFs are deployed to the hardware resources based upon the deployment policy.

Allocating hardware resources for virtual network function deployment at sites based on templates

Aspects of the subject disclosure may include, for example, monitoring status of a network configured to provide end-to-end delivery of services to priority user equipment authorized according to a prioritized class of service and other user equipment not authorized for the prioritized class of service. The prioritized class of service extends a reserved network capacity for end-to-end delivery of a service via first domain including first network slices and a second domain including second network slices. A network interruption compromising the reserved capacity is determined based on the monitored status and integration of an alternate group of slices into one of the first network domain, the second network domain, or both to replace the compromised group of slices, is orchestrated. Integration of the alternate group of slices restores reserved network capacity according to the prioritized class of service. Other embodiments are disclosed.

Network resiliency and impact coordination

Aspects of the subject disclosure may include, for example, receiving workload requests for tenant devices providing services to user devices; partitioning node resources to facilitate network slicing in a software defined network to provide the services; determining that the workload requests exceed available resources of the node; and outsourcing a portion of a load to one or more neighboring nodes, where the outsourcing is performed in coordination with a regional orchestrator and a SDN intelligence module. Other embodiments are disclosed.

System and method of software defined network enabled slicing as a service utilizing artificial intelligence

Concepts and technologies are disclosed herein for providing a network virtualization policy management system. An event relating to a service can be detected. A first policy that defines allocation of hardware resources to host the virtual network functions can be obtained, as can a second policy that defines deployment of the virtual network functions to the hardware resources. The hardware resources can be allocated based upon the first policy and the virtual network functions can be deployed to the hardware resources based upon the second policy.

Network virtualization policy management system

Virtual machine server clusters are managed using self-healing and dynamic optimization to achieve closed-loop automation. The technique uses adaptive thresholding to develop actionable quality metrics for benchmarking and anomaly detection. Real-time analytics are used to determine the root cause of KPI violations and to locate impact areas. Self-healing and dynamic optimization rules are able to automatically correct common issues via no-touch automation in which finger-pointing between operations staff is prevalent, resulting in consolidation, flexibility and reduced deployment time.

Self-healing and dynamic optimization of VM server cluster management in multi-cloud platform

A system for managing systems with interrelated microservices with self-assembling and self-configuring microservices includes a plurality of host resource services microservices, a plurality of directory services microservices and a plurality of tenant microservices. Each of the microservices is provided with an API with a standard way to identify the API and its components, which together identify the functionality of the API in a common way. The host resources service microservice are started up in a host. A determination is made whether there is a directory service microservice running in the host and, if there is none, then spinning up a directory service microservice. A plurality of tenant microservices are installed and a determination is made whether all tenant microservices are running, If not, then copies of the tenant microservices that are not running are spun up.

Self-assembling and self-configuring microservices

Growing community of inventors

Morganville, NJ, United States of America

David H Lu