International Business Machines Corporation

Kyndryl, Inc.

Abhishek Jain

Sasikanth Eda

Sandeep R Patil

Sarvesh S Patel

Sarbajit Kumar Rakshit

Subhojit Roy

Gandhi Sivakumar

Kushal S Patel

Venkata Vara Prasad Karri

Joseph W Dain

Deepak R Ghuge

Nilesh Prabhakar Bhosale

Daniel De Souza Casali

Gregory Tad Kishi

Sachin Chandrakant Punadikar

Manojkiran Eda

Dileep Dixith

Huzefa Pancha

Rajan Dilavar Mithani

Anil Laxman Palled

Martin G Keen

Anbazhagan Mani

Christina Ann Lara

Ashish Pandey

Asmahan A Ali

Karthik Iyer

Asmahan Ali

Adriana Pellegrini Furnielis

Nicolas Jean-Baptiste Joly

Jeremy R Fox

Frank Bernhard Schmuck

Saraswathi Sailaja Perumalla

Garfield Winston Vaughn

William W Owen

Felipe Knop

Venkateswara Rao Puvvada

Owen Theodore Anderson

Shrikant V Karve

Enci Zhong

Frank N Lee

Abhishek Dave

Amey P Gokhale

Ashish Chaurasia

Subramaniyan Nallasivam

Praduemn K Goyal

Sowjanya Rao

Sanjay Vipin Gandhi

Pushkaraj Balasaheb Thorat

Sandeep Naik

Aakanksha Mathur

Pardeep Singh

Digvijay Ukirde

Vikrant Malushte

Khader Saheb Shaik

Pravin Kailas Mahajan

Richard Earl Harken

Christopher J Vollmar

Chhavi Agarwal

Eduardo Ferreira Tomaz

Mustafa Mah

Rakesh Chinhara

A computer resource disparity is detected. The computer resource disparity is related to performing a computing task. The computer resource disparity is located proximate to a first location. A set of one or more autonomous vehicles capable of being adjacent to the first location is identified. An autonomous vehicle computing inquiry is generated. The inquiry is generated based on the first location and based on the computer resource disparity. The autonomous vehicle computing inquiry is transmitted based on the first location. An autonomous vehicle status is received in response to the autonomous vehicle computing inquiry that includes a set of one or more computing resources of the set of autonomous vehicles. A first autonomous vehicle of the set of autonomous vehicles is assigned to perform the computing task. The assignment is based on the set of computing resources of the set of autonomous vehicles.

Edge computing autonomous vehicle infrastructure

An augmented reality (AR) container orchestration system includes computer hardware including an AR system and a container orchestration platform. The AR system is configured to perform identifying a container to be deployed, displaying, to a user, a grid having a plurality of cells each of which are configured to receive a representation of the container, and redisplaying, using the AR system and to the user, the grid based upon a movement of the representation of the container to a particular position within the grid. The container orchestration platform is configured to perform identifying possible configurations for the container, and configuring the container based upon the movement of the representation of the container to the particular position. The includes a plurality of axes, and each axis of the plurality of axis represents a different configuration-related parameter of the configurations for the container.

Intelligent container configuring using augmented reality

A method, system, and computer program product for context relevant data migration to a cloud server with job rescheduling are provided. The method identifies a contextual situation associated with at least a portion of data sets available for migration to a cloud server by a set of data sources. The method identifies an application associated with the contextual situation. A set of use characteristics are determined for the data sets based on the contextual situation and the application. Bandwidths are determined for one or more data sources. One or more data sets are selected for prioritization based on a contextual relevance, the set of use characteristics, and the bandwidths for the one or more data sources. The method schedules a job processing sequence of the selected one or more data sets based, at least in part, on the contextual relevancy, the set of use characteristics, and the bandwidths.

Context relevant data migration and job rescheduling

Provided are a computer program product, system, and method for determining an encryption technique for a modified data object to backup. Deep data inspection is performed on an object using a natural language processing module to determine facets for the object. The facets provide facet values for instances of sensitive information in the object. The facet values for the object are processed to determine a cumulative facet value. A determination is made of an encryption technique comprising one of a plurality of different encryption techniques to use on the object based on the cumulative facet value. The determined encryption technique is used to encrypt the object to transfer to a backup storage.

Determining facet values for sensitive information in a data object to determine an encryption technique to encrypt the data object for backup

Provided is a computer-implemented method, system, and computer program product for predictive service orchestration using threat modeling analytics. A processor may identify a plurality of attributes of a client computing environment. The processor may collect threat modeling content for a plurality of vendor computing environments. The processor may analyze the threat modeling content for the plurality of vendor computing environments. The processor may compare the analyzed threat modeling content for the plurality of vendor computing environments with the plurality of attributes of the client computing environment. The processor may generate, based on the comparing, a client threat model for the client computing environment.

Predictive service orchestration using threat modeling analytics

A method, computer program product, and computer system for using agents at a data lake to assist training, testing, and deploying a model for predicting one or more outputs from specified input to the model, using agents at the data lake. The agents identify at the data lake: hardware resources, a first portion of model input data for training and testing the model, a training language, and a first software resource. A first execution of the first software resource is triggered to split the first portion of the model input data into training input data and testing input data. A first portion of the hardware resources is identified for training and testing the model. A second software resource stored at the data lake is identified for optimizing the model in accordance with an optimization algorithm. Training the model at the data lake is triggered.

Agent assisted model development

Computer-implemented methods for management of data collection devices. Aspects include creating a cluster of data collection devices and a distributed meta-key manager for the cluster and providing an authentication key for each data collection device to access the distributed meta-key manager. Aspects also include collecting and storing data by one or more of the data collection devices and periodically perform a quorum check for each data collection device of the cluster. Aspects further include updating an operational mode of each data collection device based on the quorum check and offloading the stored data from a data collection device based on successful verification of the stored data and the operational mode of the data collection device.

Zero trust management for data collection devices

A method, system, and computer program product for preventing resource exploitation in mobile devices are provided. The method receives a resource request from a mobile application stored on a mobile computing device. A device context is determined for the mobile computing device. A resource consumption context is determined for the mobile computing device based on the device context and resource consumption of one or more mobile applications operating on the mobile computing device. In response to the resource request, the method generates a set of resource consumption profiles based on the device context and the resource consumption context. A resource consumption profile is selected for the mobile application from the set of resource consumption profiles. The method manages consumption of resources, of the mobile computing device, by the mobile application based on the selected resource consumption profile.

Preventing resource exploitation in mobile devices

A method for assigning and migrating files among tiers within a unified storage environment. The method includes one or more computer processors determining a plurality of files stored within a tiered storage environment that are accessed by a set executing applications. The method further includes determining that one or more files of the plurality of files are accessed utilizing two or more protocols. The method further includes identifying a group of two or more tiering policies utilized within a tiered storage environment. The method further includes determining protocol-based heat count values corresponding the plurality of files. The method further includes storing the plurality of files among tiers of the tiered storage environment based on the identified group of two or more tiering polices and the protocol-based heat count values corresponding to the plurality of files.

Storage tiering within a unified storage environment

In an approach to improve mobile computation while traveling by dynamically generating one or more routes base on computing resource requirements of one or more endpoint devices. Embodiments identify, in real time, a plurality of autonomous vehicles, wherein the plurality of autonomous vehicles are traveling along a common route. Further embodiments, adjust, in real time, relative positions and speeds of the plurality of autonomous vehicles to maintain the plurality of autonomous vehicles within a predetermined geographic area while traveling along the common route, and wherein the predetermined geographic area is sufficient to collectively provide an amount of edge computing resources to satisfy one or more computing resource requirements of the one or more endpoint devices located within a first autonomous vehicle. Additionally, embodiments adjust, in real time, a route of the first autonomous vehicle based on the common route of the plurality of autonomous vehicles providing the edge computing resources.

Dynamic route recommendation based on mobile computation

A computer-implemented method according to one aspect includes determining and storing characteristics of a plurality of cloud vendors; dividing a workload into a plurality of logical stages; determining characteristics of each of the plurality of logical stages; and for each of the plurality of logical stages, assigning the logical stage to one of the plurality of cloud vendors, based on a comparison of the characteristics of the plurality of cloud vendors to the characteristics of the logical stage. Data migration between the cloud vendors is performed during an implementation of the workload to ensure data is located at necessary cloud vendors during the corresponding tasks of the workload.

Implementing workloads in a multi-cloud environment with data migration between cloud vendors

An augmented reality (AR) development system includes computer hardware including an AR system and a development server. The development server is configured to perform identifying a plurality of microservices to be deployed into an architecture, at least one integration layer in the architecture, and at least one application programming interface (API) layer in the architecture. The AR system is configured to perform generating a first visualization of the architecture that includes: a plurality of representations respectively corresponding to the plurality of microservices to be deployed in the architecture and a plurality of distinct and visually identifiable locations that respectively correspond to a unique combination of a specific API layer and a specific integration layer; receiving an indication for modifying a placement of one of the plurality of microservices within the first visualization; and generating a second visualization of the architecture based upon the indication.

Microservice deployment in integration and API layers using Augmented Reality

A computer-implemented method, according to one embodiment, includes: receiving, at a clustered filesystem from a formatted filesystem, a request to perform a data integrity check for a portion of data. A determination is made as to whether the request includes a filesystem type of the portion of data, and in response to determining that the request includes a filesystem type of the portion of data, another determination is made as to whether the clustered filesystem supports the data integrity check for the filesystem type. In response to determining the clustered filesystem supports the data integrity check, another determination is made as to whether the portion of data is currently available. Furthermore, the computer-implemented method includes causing the data integrity check to be performed in response to determining that the portion of data is currently available. Results of performing the data integrity check are also sent to the formatted filesystem.

Deduplicating data integrity checks across systems

A computer-implemented to control access to data on an off-premises storage system. The method includes defining an access policy for a plurality of files in a file system stored in an on-premises storage system. The method further includes registering the access policy with a first off-premises storage system. The method also includes creating a resource-based cloud access policy based on an on-premise access policy. The method includes determining a set of files from the plurality of files to migrate to the off-premises storage. The method also includes obtaining, for the set of files, an access policy as access metadata. The method further includes migrating the set of files and the access metadata to the off-premises storage.

Management of access control in multi-cloud environments

A method, computer system, and a computer program product for securing visible data is provided. The present invention may include encrypting an on-screen data rendered on a display of an endpoint device. The present invention may also include authenticating an external decryption device within a periphery defined by the endpoint device. The present invention may further include decrypting the encrypted on-screen data on the authenticated external decryption device.

Securing visible data

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Baraut, India

Abhishek Jain