Emc IP Holding Company LLC

Cisco Technology, Inc.

Dell Products Inc.

Future Wei Technologies, Inc.

Other

Kenneth Durazzo

Yu Cao

Victor Fong

Stephen J Todd

Junping Zhao

Xiaoyan Guo

Simon Tao

Sanping Li

Mohamed Khalid

Zhen Jia

Paul M Quinn

Xuebin He

Ricky Yuxi Sun

Accela Yilong Zhao

Xiaohong Qin

Suresh Kumar

Assaf Natanzon

Kun Wang

Jinpeng Liu

Kevin Xu

Andrew George Harvey

Satish K Gannu

Zijia Wang

Shree N Murthy

Danqing Sha

Randall R Stewart

Aamer Saeed Akhter

John T Cardente

Michael Robillard

Robert E Gleichauf

Layne Lin Peng

Richard Mandfred Pruss

Zhe Dong

Sheppard David Narkier

Ryan Wallner

Yunxia Chen

Michael Estrin

Yicheng Tao

Sonali Mihir Sambhus

Leon A Frazier

Shamail Tahir

Barry Raveendran Greene

Rajiv Asati

Alpesh S Patel

Rômulo Teixeira De Abreu Pinho

Bin He

Kenneth J Taylor

Sravan Vadlakonda

James Robert King

Zhisong Liu

John Roese

Eric Joseph Bruno

Anzhou Hou

Frederick Juergens Baker

Rohini Kasturi

Peter Lei

Saravanakumar Rajendran

Govind Prasad Sharma

Zhi Ying

Seth Jacob Rothschild

Vaibhav Khanduja

Ian Foo

Robert A Lincourt

Ming Chi Chen

Alex Robbins

Darrel Jay Lewis

Sunil Cherukuri

Nalinkumar Mistry

Richard Manfred Pruss

Dean Cheng

Min Chao Zhao

Vivek Datar

Patrick Grossetete

Jack Harwood

Ciprian Pompiliu Popoviciu

Yu-Cheng Shao

Malini Bhattacharjee

Brendan Burns Healy

Nipa Kumar

Vivian Wenwen Gao

Orlando Xavier Nieves

An Ho

Grace Yan Cui

Thinh Lam Truong Nguyen

Edgardo Robles

Cang Dai

Aman Wangde

Patrick J Mullaney

Xing Yang

Armin Current

Ramakrishnan Rajamoni

Daniel John Floreani

Lloyd Harvey Wood

Techniques are disclosed for schedule management for machine learning model-based processing in a computing environment. For example, a method receives a machine learning model-based request and determines a scheduling decision for execution of the machine learning model-based request. Determination of the scheduling decision comprises identifying, based on one or more metrics, at least one cluster from a plurality of clusters as an execution environment in which the machine learning model-based request is to be executed. The machine learning model-based request may then be forwarded to the at least one identified cluster for execution.

Schedule management for machine learning model-based processing in computing environment

One example method includes receiving, by a hybrid classical-quantum computing system, data from a node of a data confidence fabric, processing the data to create processed data, generating one or more confidence scores relating to the processed data, and making the one or more confidence scores and the processed data available to an end user. The hybrid classical-quantum computing system may also be a node of the data confidence fabric and may perform classical and/or quantum computing operations on the data.

Classical-quantum data confidence fabric

One example method includes receiving a hybrid/classical algorithm, determining a runtime characteristic of the hybrid/classical algorithm, based on the runtime characteristic, checking a memory availability for execution of the hybrid/classical algorithm, when adequate memory is not available to support execution of the hybrid/classical algorithm, modifying a classical/quantum memory fabric to provide enough memory to support execution of the hybrid/classical algorithm, and orchestrating the hybrid classical/quantum algorithm to an execution environment that includes the classical/quantum memory fabric.

Intelligent classical/quantum extended memory fabric

One example method includes receiving a computation workflow defined by a graph that includes quantum computing nodes, receiving a catalogue of quantum computing instances that are available in a hybrid classic-quantum computation infrastructure, transforming the graph to create a first graph transformation, and each of the quantum computing nodes is assigned a respective candidate resource allocation that identifies candidate resources operable to execute a respective quantum algorithm associated with that quantum computing node, and the transforming is performed using information from the catalogue, and optimizing the computation workflow by selecting, for each of the quantum computing nodes, a resource from the candidate resource allocation associated with that quantum computing node, and the optimizing includes transforming the first graph transformation to create a second graph transformation that specifies the selected resources for each node.

Intelligent orchestration of classic-quantum computational graphs

A method comprises generating a distilled dataset from an input dataset, wherein the input dataset comprises a plurality of first data samples, and the distilled dataset comprises a plurality of second data samples. In the method, an input data sample is received, and similarities are computed between the input data sample and respective ones of the plurality of second data samples. The method further comprises selecting one or more second data samples of the plurality of second data samples based, at least in part, on the computed similarities, and retrieving one or more first data samples of the plurality of first data samples from the input dataset based, at least in part, on the selected one or more second data samples. The generating is performed, at least in part, using one or more machine learning models.

Content analysis and retrieval using machine learning

Techniques for training data protection in an artificial intelligence model execution environment are disclosed. For example, a method comprises executing a first partition of an artificial intelligence model within a secure execution area of an information processing system and a second partition of the artificial intelligence model within a non-secure execution area of the information processing system, wherein data at least one of obtained and processed in the first partition of the artificial intelligence model is inaccessible to the second partition of the artificial intelligence model. Communication between the first partition and the second partition may be enabled via a model parallelism-based procedure. Data obtained in the secure execution area may comprise one or more data samples in an encrypted form usable to train the artificial intelligence model.

Training data protection for artificial intelligence model in partitioned execution environment

One example method includes performing a secure boot of hardware at a node of a data confidence fabric, creating an artifact that includes information concerning the secure boot, storing the artifact, receiving a data stream at the node, annotating data of the data stream with trust metadata, and associating the artifact with the data. An immutable ledger entry may be created that includes a pointer to the data, and a pointer to the artifact, and the immutable ledger entry may be accessible by an application.

Secure enclave pathing configuration for data confidence fabrics

An apparatus comprises a first processing device, the first processing device comprising a physical hardware controller configured for coupling with a second processing device. The first processing device is configured to identify remote security service instances attached to the second processing device and to initiate, at the first processing device, one or more network emulation modules for the remote security service instances attached to the second processing device that emulate physical network interface devices configured for attachment to the second processing device. The first processing device is also configured to provision the remote security service instances to the second processing device by utilizing hardware resources of the physical hardware controller to analyze network traffic associated with the second processing device, to modify at least a portion of the network traffic, and to provide the modified network traffic to the second processing device via the emulated physical network interface devices.

Physical hardware controller for provisioning security services on processing devices

Methods, apparatus, and processor-readable storage media for automatically processing user request data using artificial intelligence techniques are provided herein. An example computer-implemented method includes generating, using a chatbot during a communication session with a user, answers to user requests by processing the user requests using a first set of artificial intelligence techniques associated with the chatbot; generating knowledge base-related predictions associated with the user requests by processing the user requests using a second set of artificial intelligence techniques associated with a knowledge base; calculating at least one score associated with at least a portion of the answers and the user requests based on qualifying values computed in connection with at least one of generating the answers and generating the knowledge base-related predictions; and performing one or more automated actions based on the at least one calculated score.

Automatically processing user request data using artificial intelligence techniques

Techniques for integrating a trusted execution platform with a function-based service framework are disclosed. For example, a method obtains an application program comprising a first set of one or more functions for execution within a secure execution area of a function-based service framework and a second set of one or more functions for execution within a non-secure execution area of the function-based service framework. A client attests an attestation delegator and the attestation delegator attests one or more secure containers prior to receipt of a function execution request to execute a function in the function-based service framework.

Function-based service framework with trusted execution platform

One example method includes performing dynamic access control in a computing network. A computing environment is configured such that an application can access a service without specifying secrets. The secrets needed to access the service are obtained and stored in a credential store. The secrets can be obtained using the service mesh in a manner that isolates the application from the secrets.

Dynamic access control in service mesh with service broker

One example method includes transmitting a request for a container image to a registry, receiving metadata associated with the container image, wherein the metadata allows a controller to mount an empty filesystem on a host machine, starting a container from the container image without receiving all files associated with the container image, receiving files, from a container server, needed by the container based on an access sequence associated with the container. This allows a container to be started without downloading the entire container image and also conversed bandwidth by providing the files as needed based on the manner in which the container accesses files during execution.

Intelligent filesystem for container images

Techniques for function execution environment selection for a decomposed application are provided. In one example, an apparatus comprises at least one processing platform configured to execute a portion of an application program in a first virtual computing element, wherein the application program comprises one or more portions of marked code, receive a request for execution of one of the one or more portions of marked code, decide whether to execute the portion of marked code identified in the request in the first virtual computing element or in a second virtual computing element, determine an execution environment from one or more execution environments specified in the marked code for the second virtual computing element to execute the marked code, when it is decided to execute the portion of the marked code in the second virtual computing element, and cause the portion of marked code identified in the request to be executed.

Function execution environment selection for decomposed application

An apparatus comprises a first processing device, the first processing device comprising a physical hardware controller configured for coupling with a second processing device. The first processing device is configured to identify one or more remote storage service instances attached to the second processing device, and to initiate storage emulation modules for the remote storage service instances attached to the second processing device, the storage emulation modules emulating one or more physical storage devices configured for attachment to the second processing device. The first processing device is also configured to provision the remote storage service instances to the second processing device by processing input/output requests that are directed to one or more storage volumes of the emulated physical storage devices utilizing hardware resources of the physical hardware controller and providing processing results of the input/output requests to the second processing device via the emulated physical storage devices.

Physical hardware controller for provisioning remote storage services on processing devices

Techniques are disclosed for generating a virtual representation (e.g., one or more digital twin models) of a multi-access edge computing system environment, and managing the multi-access edge computing system environment via the virtual representation. By way of example only, such techniques enable understanding, prediction and/or optimization of performance of applications and/or systems operating in the multi-access edge computing environment.

Digital twin architecture for multi-access edge computing environment

Growing community of inventors

San Ramon, CA, United States of America

Kenneth Durazzo