International Business Machines Corporation

Kyndryl, Inc.

Abhishek Malvankar

John Michael Ganci, Jr

Shikhar Kwatra

Jeronimo Irazabal

Carlos A Fonseca

Charles Evan Beller

Sarbajit Kumar Rakshit

Ashish Kundu

Nitin Gaur

Clement Decrop

Naga A Ayachitula

Marquita May Ellis

Kushal S Patel

Nadiya Kochura

Dulce Beatriz Ponceleon

Suresh N Chari

Seng Chai Gan

Ashok Pon Kumar Sree Prakash

Indervir Singh Banipal

Louis Degenaro

Jennifer M Hatfield

Umamaheswari C Devi

Thomas Jefferson Sandridge

Chen Wang

Edward Adam Epstein

Diana Jeanne Arroyo

Burn Lewin Lewis

Sara Rosenthal

Saurabh Pujar

Mahesh Gajwani

A computer hardware system includes a context controller configured to manage recovery for a workload within a cloud environment. A plurality of compute nodes are initially allocated to the workload, as a current allocation, within a context of the cloud environment. A failure within the current allocation is identified. Responsive to the failure being identified, a plurality of costs respectively associated with each of a plurality of recovery options for the workload are evaluated. A lowest-cost option, from the plurality of recovery options being evaluated, is executed. The recovery options being evaluated includes both recovery options that include performing the workload within the current context and recovery options that include performing at least a portion of the workload in at least one secondary context of the cloud environment. A resource controller node associated with the context controller is communicated with to identify the at least one secondary context.

Resource-aware workload reallocation across cloud environments

A method and system allocating computing resources according to a trained machine learning model that includes receiving a request to accommodate a particular workload that involves a first threshold amount of computing resources implemented by at least one computing cluster. An identified a set of computing clusters, that have computing resources available for allocating from the identified set of computing clusters to the at least one computing cluster to satisfy the first threshold amount of computing resources, is received from a database. A trained deep-reinforcement learning model is applied to generate a policy for allocating the available computing resources from the identified set of computing clusters to the at least one computing cluster. It is verified that the generated policy satisfies a threshold according to one or more predetermined criteria. The computing resources are allocated from the set of computing clusters to the at least one computing cluster.

Allocating computing resources using a machine learning model

An embodiment includes tokenizing a code segment generated by an integrated development environment responsive to user input, the tokenizing resulting in a tokenized code segment. The embodiment generates a search query by analyzing the tokenized code segment using a trained neural network. The embodiment executes a querying process that searches a knowledge base using the search query for a hardware requirement associated with the code segment, and detects a search result that conveys the hardware requirement as including a specialized hardware resource. The embodiment generates a time allotment associated with execution of the code segment using a second trained neural network that predicts the time allotment based on the tokenized code segment. The embodiment issues an instruction associated with the code segment to a hardware scheduler causing the hardware scheduler to allocate the specialized hardware resource for an amount of time corresponding to the time allotment.

Cognitive allocation of specialized hardware resources

An artificial intelligence (AI) platform to support selective replacement of one or more image layers of a container image build. A metadata file is subject to natural language processing and one or more corresponding vector representations are created and subject to evaluation by a set of artificial neural networks (ANNs). A first ANN assesses each vector representation with respect to compliance and operability, and the second ANN selectively assesses the vector representation(s) with respect to similarity with one or more compliant vector representations. In response to the assignment of the second score, at least one vector representation of the received metadata file is selectively replaced with at least one compliant vector representation. The metadata file is selectively provisioned with the selectively replaced vector representation(s).

Risk assessment of a container build

A plurality of ledger nodes within a blockchain infrastructure is continuously updated via a set of conduit nodes. The set of conduit nodes share a secure provision ledger. The secure provision ledger includes a set of updates for the blockchain infrastructure. Each conduit node within the set propagates the set of updates to a subset of the plurality of ledger nodes.

Ensuring secure provisioning of blockchain infrastructure

A computer-implemented method for configuring a blockchain network, a computer program product for integrating device identity into a permissioning framework of a blockchain network, and a blockchain network. One embodiment may comprise registering a device at a delineate node of a blockchain network, creating, by a processor of the delineate node based on the registering, a profile for the device; an performing, by the processor of the delineate node, a pass-through service for the device. The registering may include receiving, by a network interface, an immutable device identity from the device.

Integrating device identity into a permissioning framework of a blockchain

An artificial intelligence (AI) platform to support optimization of workload scheduling in a distributed computing environment. Unstructured data corresponding to one or more application artifacts related to a workload in the distributed computing environment is leveraged. NLP is applied to the unstructured data to identify one or more host requirements corresponding to the application artifacts. One or more hosts in the computing environment compatible with the identified host requirements are selectively identified and compatibility between the application artifacts and the identified hosts is assessed. The workload is selectively scheduled responsive to the selective host identification based on the assessed compatibility. The scheduled workload is selectively executed on at least one of the selectively identified hosts responsive to the assessment workload compatibility.

Optimization of workload scheduling in a distributed shared resource environment

An artificial intelligence (AI) platform to support optimization of container builds and virtual machine mounts in a distributed computing environment. A provisioning file is subject to natural language processing (NLP) and a corresponding vector representation of the file is created and subject to evaluation by a set of artificial neural networks (ANN). A first ANN assesses the representation of the file with respect to compliance and operability, and the second ANN selectively assesses the representation of the file with respect to provisioning efficiency. The provisioning file is selectively process based on the provisioning efficiency, with the processing directed at provisioning a container build or mounting a VM.

An example operation may include one or more of: receive a first set of annotation information, determine a first approval decision for the first set of annotation information, record the first approval decision in a blockchain, retrieve a first set of approval decisions from the blockchain, generate a consensus based on the first set of approval decisions, and record the consensus in the blockchain.

Smart data annotation in blockchain networks

Aspects of the present disclosure relate to generating and visualizing subgroups of participants within web-based conferences. Participant data for each of a plurality of participants attending a web-based conference can be obtained. The participant data can be analyzed for each of the plurality of participants. Each of the participants can be mapped to a subgroup of two or more subgroups based on the analysis. The two or more subgroups can then be visualized within the web-based conference to the participants.

Visualizing web conference participants in subgroups

A processor may run a background process to identify a first task being initiated by a first user on a device, where the first task is associated with a first application. The processor may identify the first user of the device. The processor may analyze one or more interactions of the first user associated with the first application on the device. The processor may allocate, based at least in part on identification of the first user, identification of the first task, or analysis of the one or more interactions of the first user, computing resources to one or more hardware components on the device.

Cognitive processing resource allocation

Embodiments are provided for intelligent recommendations for program code. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include an evaluation component that determines that first program code pertains to a defined category representing a defined cost to execute the first program code by a cloud computing service. The computer-executable components also can include a recommendation component that generates a recommendation for second program code that satisfies a similarity criterion with respect to the first program code. The second program code pertains to a category representing a cost to execute the second program code by the cloud computing service, where the cost is less than the defined cost.

Intelligent recommendations for program code

A computer-implemented method, system, and computer program product for generating an aggregated user interface display. The aggregated user interface display includes a physical display of a computing device and a projected display from an electronic pen. Operator interactions with the aggregated user interface display are saved as historical information. The historical information is used to determine a configuration for the aggregated user interface display. The computing device and the electronic pen are controlled to implement the configuration for the aggregated user interface display.

Aggregated multidimensional user interface display with electronic pen for holographic projection

An example operation includes one or more of receiving a request associated with a software library, identifying metadata of the software library, determining, via chaincode, whether data of the software library has been previously stored on a blockchain, and in response to a determination that the software library has not been previously stored, storing the identified metadata of the software library and a notarization value generated by the chaincode within the blockchain.

Blockchain-based software library access and usage management

The present invention may include an embodiment that receives a deployment declaration in a natural language. The embodiment may detect one or more sequencing entities and one or more parameter entities using trained natural language processing. The embodiment may sequence a configuration file based on the one or more sequencing entities. The embodiment may determine a plurality of configuration parameters in the sequenced configuration file. The embodiment may substitute a configuration parameter from the plurality of configuration parameters of the sequenced configuration file with the one or more parameter entities. The embodiment may align the plurality of configuration parameters of the sequenced configuration file based on organization compliance data and deploys a tuned cloud service using the sequenced configuration file.

Growing community of inventors

White Plains, NY, United States of America

Abhishek Malvankar