Emc IP Holding Company LLC

Dell Products Inc.

Victor Fong

Xuebin He

Kenneth Durazzo

Amy N Seibel

Megan A Murawski

Zhen Jia

Eric Joseph Bruno

Michael Robillard

Anzhou Hou

Robert A Lincourt

Kathleen Moriarty

Malini Bhattacharjee

Stephen J Todd

James Robert King

Alex Robbins

Zijia Wang

Jiacheng Ni

John S Harwood

Robert Anthony Lincourt, Jr

Qiang Chen

Rômulo Teixeira De Abreu Pinho

Bin He

Zhisong Liu

Danqing Sha

William Jeffery White

Himanshu Arora

Douglas L Farley

Seth Jacob Rothschild

Tianxiang Chen

Geoffrey A Reid

Christopher S MacLellan

Brendan Burns Healy

Kranti Uppala

Orlando Xavier Nieves

Sarah Evans

Nisanth Mathilakath Padinharepatt

Edgardo Robles

Thinh Lam Truong Nguyen

Aman Wangde

Prasenjit Roy

An apparatus comprises a processing device configured to identify one or more dependent services for a workload deployed in a first computing cluster of a multi-cluster computing environment and to select, utilizing a global service catalog aggregating service information for sets of available services offered by at least two computing clusters of the multi-cluster computing environment, a second computing cluster of the multi-cluster computing environment to utilize for provisioning at least a given one of the one or more dependent services for the workload. The processing device is further configured to provision the given dependent service on a multi-cluster service mesh associated with the multi-cluster computing environment by configuring the multi-cluster service mesh to permit access by the workload on the first computing cluster of the multi-cluster computing environment to the given dependent service on the second computing cluster of the multi-cluster computing environment.

Global service catalog for provisioning services on a multi-cluster service mesh of a multi-cluster computing environment

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

Techniques are disclosed for multi-factor prediction of computing resources for algorithm execution. For example, a method comprises obtaining a set of factors associated with an algorithm configured to transform one or more two-dimensional images into one or more three-dimensional models. The method further comprises computing an estimated computing power value based on the set of factors. The method then comprises scheduling execution of the algorithm on one or more computing resources based on the estimated computing power value.

Multi-factor prediction of computing resources for two-dimensional to three-dimensional modeling acceleration

A method comprises receiving a request for at least one network interface card resource, and identifying one or more network interface cards comprising the at least one network interface card resource. In the method, at least one virtualized computing resource image is received, and a workload is executed using an instance of the at least one virtualized computing resource image running on the one or more network interface cards. Workload execution includes implementation of a binding mechanism using one or more control groups and a given protocol. In addition, a cluster including one or more nodes comprising the identified one or more network interface cards is generated.

Management and utilization of network interface card resources for workload execution

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

One example method includes creating an image definition file, using the image definition file to create an image that is deployable to an edge device, copying an agent into the image, stripping any user passwords out of the image, and removing any unnecessary packages from the image, and after the stripping and the removing, the image becomes an immutable image. The immutable image may then be deployed to a group of edge devices, and one or more layers of the immutable image may be updated by the agent.

Immutable image for deployment to edge devices

Techniques for constructing and otherwise managing knowledge graphs in information processing system environments are disclosed. For example, a method comprises the following steps. The method collects data from a plurality of data sources. The method extracts structured data and unstructured data from the collected data, wherein unstructured data is extracted using an unsupervised machine learning process. The method forms a plurality of sub-graph structures comprising a sub-graph structure for each of the data sources based on at least a portion of the extracted structured data and unstructured data. The method combines the plurality of sub-graph structures to form a combined graph structure representing the collected data from the plurality of data sources. The resulting combined graph structure is a comprehensive knowledge graph.

Knowledge graph management based on multi-source data

A method includes aggregating edge resources of a plurality of edge computing sites of an edge utility system, with the aggregated edge resources including edge resources provided by at least one user for utilization by one or more other users. The method further includes utilizing one or more portions of the aggregated edge resources to execute one or more workloads of the one or more other users, metering the utilization of the one or more portions of the aggregated edge resources in conjunction with the execution of the one or more workloads, and committing information characterizing at least the metered utilization of the aggregated edge resources to a distributed ledger.

Edge utility system with distributed ledger verification for dynamic aggregation of edge resources

Edge security is disclosed. Hosts included in a distributed infrastructure are equipped with data processing units that may be centrally managed using a control plane. Security policies can be selectively distributed to the hosts and localized at the hosts. Reactions to security violations can be initiated at the hosts in substantially real-time. The security policies may take various forms, including rule-based security policies and inference mode-based security policies.

Infrastructural edge security as a service

One example method includes determining that local resources at an edge site are inadequate to support performance of a function needed by software running on the edge site, invoking a client agent, in response to invoking the client agent, receiving an execution manifest, determining, by the client agent, where to execute the function, wherein the determining comprises identifying a target execution environment for the function and the determining is based in part on information contained in the execution manifest, and transmitting, by the client agent, the execution manifest to a server agent of the target execution environment, and the execution manifest facilitates execution of the function in the target execution environment.

Edge function bursting

An apparatus comprises at least one processing device configured to establish, utilizing a communication network, a remote collaboration session between first and second computing devices associated with first and second users, and to determine data to be exchanged between the first and second computing devices as part of the remote collaboration session. The first and second computing devices each comprise at least one of an augmented reality and a virtual reality computing device. The processing device is also configured to generate different logical network slices of the communication network, each logical network slice having a set of network characteristics associated with at least one type of the data to be exchanged between the first and second computing devices as part of the remote collaboration session. The processing device is further configured to communicate the different types of data between the first and second computing devices utilizing the logical network slices.

Remote collaboration between users utilizing at least one of augmented reality and virtual reality computing devices

Techniques are disclosed for chain 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 utilizing a set of one or more scheduling algorithms and comparing results of at least a portion of the set of one or more scheduling algorithms to identify execution environments of a computing environment in which the machine learning model-based request is to be executed. The identified execution environments may then be managed to execute the machine learning model-based request.

Growing community of inventors

Medford, MA, United States of America

Victor Fong