Intel Corporation

Novell, Inc.

Emc Corporation

Mcafee, Inc.

Kshitij Arun Doshi

Francesc Guim Bernat

Ned M Smith

Timothy Verrall

Mark A Schmisseur

Rajesh Poornachandran

Kapil Sood

Elmoustapha Ould-Ahmed-Vall

Christian Maciocco

Satish Chandra Jha

Karthik Kumar

John Joseph Browne

Andrew J Herdrich

Vincent J Zimmer

Christopher J Hughes

Nageen Himayat

Srikathyayani Srikanteswara

Bret L Toll

Quinn Able Jacobson

Ravi L Sahita

Glen J Anderson

Hong Wang

Ravishankar Iyer

Michael Lemay

Gad Shlomo Sheaffer

Per H Hammarlund

Raj K Ramanujan

David M Durham

Ren Wang

Ali-Reza Adl-Tabatabai

Sanjeev N Trika

Thomas Willhalm

Alex Nayshtut

Ignacio Javier Alvarez

Rahul Khanna

Vinodh Gopal

Jill MacDonald Boyce

Mohan J Kumar

Rita H Wouhaybi

Suraj Prabhakaran

Vadim Sukhomlinov

Daniel Rivas Barragan

Alexander Bachmutsky

S M Iftekharul Alam

Vesh Raj Sharma Banjade

Tarun Viswanathan

Suleyman Sair

Marcos Emanuel Carranza

Raghu R Kondapalli

Dario Sabella

Katalin Klara Bartfai-Walcott

Miltiadis Filippou

Susanne M Balle

Mats Gustav Agerstam

Lokpraveen B Mosur

Mona Vij

Liuyang L Yang

Matthew C Merten

Deborah T Marr

John P Shen

Kuilin Clark Chen

Yi Zhang

Nilesh K Jain

Bratin Saha

Gautham N Chinya

Mark D Yarvis

Sanjay Bakshi

Evan Custodio

Eve M Schooler

Patrick G Kutch

Edwin Verplanke

Tsung-Yuan Charles Tai

Robert G Blankenship

Baiju V Patel

Igor G Muttik

Sujoy Sen

Giuseppe Beppe Raffa

Yang-Seok Choi

Sara S Baghsorkhi

Debra Bernstein

Hassnaa Moustafa

Jr-Shian Tsai

Omesh Tickoo

Utkarsh Y Kakaiya

Javier Felip Leon

Carolyn Bennion McClain

Mingqiu Sun

Maruti Gupta Hyde

Stephen T Palermo

Ian M Steiner

Ilango S Ganga

David Gomez Gutierrez

Rameshkumar G Illikkal

Narayan Ranganathan

Kenneth D Shoemaker

Ravikumar Balakrishnan

Andrey Chilikin

Jeffrey D Chamberlain

Piotr Wysocki

Dimitrios Ziakas

Examples include techniques for artificial intelligence (AI) capabilities at a network switch. These examples include receiving a request to register a neural network for loading to an inference resource located at the network switch and loading the neural network based on information included in the request to support an AI service to be provided by users requesting the AI service.

Techniques for artificial intelligence capabilities at a network device

Various systems and methods for implementing computational storage are described herein. An orchestrator system is configured to: receive, at the orchestrator system, a registration package, the registration package including function code, a logical location of input data for the function code, and an event trigger for the function code, the event trigger set to trigger in response to when the input data is modified; interface with a storage service, the storage service to monitor the logical location of the input data and notify a location service when the input data is modified; interface with the location service to obtain a physical location of the input data, the location service to resolve the physical location from the logical location of the input data; and configure the function code to execute near the input data.

Computational storage in a function-as-a-service architecture

Systems and techniques for storage-class memory device including a network interface are described herein. A write for a network communication is received by the host interface of the memory device. Here, the network communication includes a header. The header is written to a non-volatile storage array managed by a memory controller. A network command is detected by the memory device. Here, the network command includes a pointer to the header in the non-volatile storage array. The header is retrieved from the non-volatile storage array and a packet based on the header is transmitted via a network interface of the memory controller.

Storage class memory device including a network

Methods, apparatus, systems, and articles of manufacture are disclosed for data resiliency in an edge network environment. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to at least one of execute and/or instantiate the instructions to generate spectrum metadata based on spectrum data, determine a resiliency operation based on one or more resiliency requirements, generate a resiliency policy based on at least one of the resiliency operation or the one or more resiliency requirements, generate a resiliency operation map based on at least one of the resiliency policy or first identifiers of respective workloads associated with the network environment, the first identifiers including a second identifier, and, in response to identifying a FAFO event associated with the second identifier, execute the resiliency operation based on mapping the second identifier to the resiliency operation in the resiliency operation map.

Systems, apparatus, and methods for data resiliency in an edge network environment

A digital twin model is generated for physical nodes of an edge network. The digital twin model includes a digital twin for a physical node of the physical nodes. An error is identified of the physical node or the digital twin for the physical node. The digital twin model is updated to halt communication with the physical node or the digital twin of the physical node. A path is created to another physical node or a digital twin of the another physical node in the digital twin model.

Digital twin framework for next generation networks

Examples described herein relate to an Infrastructure Processing Unit (IPU) that comprises: interface circuitry to provide a communicative coupling with a platform; network interface circuitry to provide a communicative coupling with a network medium; and circuitry to expose infrastructure services to be accessed by microservices for function composition and to selectively provide a barrier to halt operation of at least one microservice based on event data from a composite node that performs the at least one microservice.

Infrastructure processing unit

Methods, apparatus, systems, and articles of manufacture are disclosed to increase resiliency in self-healing mechanisms. At least one non-transitory machine-readable medium comprises instructions that, when executed, cause at least one processor to at least partition computational resources of a first host into a primary partition and a shadow partition, the primary partition to communicate with a second host, apply a fix for the primary partition, determine if the primary partition can communicate with the second host during the application of the fix, cause, in response to the determination that the primary partition cannot communicate with the second host during the application of the fix, the shadow partition to communicate with the second host; and transfer communication with the second host from the shadow partition to the primary partition, the transfer in response to a determination that the application of the fix is complete.

Methods and apparatus to increase resiliency in self-healing mechanisms

Various aspects of methods, systems, and use cases include security-based orchestration. A method may include evaluating, within a secure environment of an untrusted device, a preamble to determine a set of security requirements. The method may include, providing, to an attestation server, an indication of security parameters for services of the untrusted device corresponding to security requirements of the set of security requirements, and in response to receiving a confirmation from the attestation server, providing a request to the untrusted device outside the secure environment to generate a trusted domain including the services.

Network supported low latency security-based orchestration

Various approaches for implementing multi-tenant data protection are described. In an edge computing system deployment, a system includes memory and processing circuitry coupled to the memory. The processing circuitry is configured to obtain a workflow execution plan that includes workload metadata defining a plurality of workloads associated with a plurality of edge service instances executing respectively on one or more edge computing devices. The workload metadata is translated to obtain workload configuration information for the plurality of workloads. The workload configuration information identifies a plurality of memory access configurations and service authorizations identifying at least one edge service instance authorized to access one or more of the memory access configurations. The memory is partitioned into a plurality of shared memory regions using the memory access configurations. A memory access request for accessing one of the shared memory regions is processed based on the service authorizations.

Multi-tenant data protection in edge computing environments

A computing device includes a direct memory access (DMA) engine coupled to a memory, a network interface, and processing circuitry. The processing circuitry is to perform a secure exchange with a second computing device to negotiate a shared encryption key, based on a request for data received via the network interface from the second computing device. The DMA engine is to retrieve the data from a storage location based on an encryption command. The encryption command indicates the storage location. The DMA engine is to encrypt the data based on the shared encryption key to generate encrypted data, and store the encrypted data in the memory.

Cryptographic data processing using a DMA engine

Systems and techniques for cross-layer automated fault tracking and anomaly detection are described herein. Anomaly data may be obtained from a plurality of layers of a network. Elements of the anomaly data may be identified that correspond to a data flow of an application executing on the network. An artificial intelligence model may be trained using the elements of the anomaly data to generate an impact score for the application. The impact score may be generated for the application by evaluating current network metrics using the artificial intelligence model. An operational component of the network may be modified based on the impact score.

Cross-layer automated fault tracking and anomaly detection

Apparatus and methods for data lifecycle management in an edge environment are disclosed herein. An example apparatus includes an operation executor to identify a first operation to be performed for a data object at an edge node in an edge environment and a second operation to be performed for the data object, the first operation different that the second operation. The example apparatus includes a time parameter retriever to retrieve a first time value associated with the first operation from a data source and a second time value associated with the second operation from the data source. The operation executor is to execute the first operation in response to the first time value and to execute the second operation in response to the second time value.

Apparatus, systems, articles of manufacture, and methods for data lifecycle management in an edge environment

A computing node includes network interface circuitry and processing circuitry. The processing circuitry assigns available computing resources to a plurality of slice contexts. Each slice context of the plurality includes resource allocations of the available computing resources associated with multiple communication networks. A first portion of the resource allocations is designated as dedicated resources and a second, remaining portion is designated as shared resources. A FAFO event associated with a workload is detected. The workload executes on a network slice instance (NSI) associated with a slice context of a subset of slice contexts. The configuration of the NSI is restored to a pre-FAFO event state based on reconfiguring one or both of the dedicated resources or the shared resources of the slice context based on the resource allocations of at least a second slice context in the subset of slice contexts.

End-to-end network slicing (ENS) from ran to core network for next generation (NG) communications

Disclosed herein are systems and methods for peer-assisted safety models for autonomous and assisted-driving vehicles. In an embodiment, a safety-model service receives a safety-model request for a safety model from a target vehicle. The safety-model service identifies, responsive to receiving the safety-model request, one or more source vehicles as safety-model input sources. The safety-model service receives safety-model data associated with the identified one or more source vehicles. The safety-model service generates, based on the safety-model request and the received safety-model data, a target-vehicle safety model for the target vehicle. The safety-model service transmits the target-vehicle safety model to the target vehicle for use by the target vehicle.

Systems and methods for brokering peer-assisted safety models for autonomous and assisted-driving vehicles

System and techniques for geographic routing are described herein. A node receives a data packet that includes map data, a sequence of geographic areas to a requestor, and a target geographic area. The node may then determine that it is within the target geographic area and start a transmit timer based on a next-hop geographic area. Here, the next-hop geographic area is determined from the sequence of geographic areas in the data packet. The node may then counts how many other nodes from the geographic area sent data packets while the transmit timer is running. When the transmit timer expires, the node may transmit a modified data packet when the number of data packets is less than a predefined threshold. Here, the modified data packet is the data packet updated to include local map data and the next-hop geographic area.

Geographic routing

Growing community of inventors

Tempe, AZ, United States of America

Kshitij Arun Doshi