Kyndryl, Inc.

International Business Machines Corporation

Pramod Vadayadiyil Raveendran

Kuntal Dey

Seema Nagar

Sougata Mukherjea

Sarbajit Kumar Rakshit

James David Cleaver

Michael James McGuire

Thuy Luong

Rajmohan

Abhishek Kaul

Manish Anand Bhide

Saiprasad Kolluri Venkata Sesha

Prateek Goyal

Sachin Gupta

Rajeev Thayyil

Laxmikantha Sai Nanduru

Kim Poh Wong

Anil Kumar Narigapalli

Saurab Basu

Dinesh K N

Arun Kandoth

Rajith P M

Sajith Kizhakke Veedu

Roy Jacob

Biju Punathichi Kandiyil

Manjit Krishnan Kappal

Suresh Kumar Thangavelu

Vikramsihn Ashok Desai

Narasimhan B Rajagopal

Rajesh Ray

Firas Bouz

Jagadesh Selvaraj

Dallas McGuire Hindle

A system, method, and computer program product for implementing artificial intelligence module communication is provided. The method includes generating models associated with communications between real-time artificial intelligence modules. Classifiers associated with the models are generated and a master real-time artificial intelligence module associated with the modules and classifiers is detected. Real-time artificial intelligence modules are detected and communications between the master real-time artificial intelligence module and the real-time artificial intelligence modules are initiated. Updated classifiers, updated models, and updated self learning software are generated. The updated self learning software is executed and a resulting query associated with a Web search is executed.

Artificial intelligence module communication

A system and associated methods that enable social media representation and collaboration platform of enterprise information technology (IT) components that enables human-to-human, human-to-machine and machine-to-machine communications to help easily understand the set of enterprise IT components, its configuration(s), its intra/inter relationship(s), better navigation and visibility across various related components, and augmenting system troubleshooting.

Social media representation and collaboration platform for enterprise it

Provided is a method, system, and computer program product for storing images across multiple distributed computing systems according to image sensitivity. The method comprises identifying an image and analyzing the image to identify sensitive information in the image. The method further comprises splitting the image into a sensitive portion and a non-sensitive portion. The method further comprises storing the sensitive portion of the image in a first distributed computing system and storing the non-sensitive portion of the image in a second distributed computing system.

Image storage across distributed computing systems

An approach is provided for determining a carbon footprint-based consumption of cloud resources. Cloud resource requirements and a carbon footprint cap of a workload of a cloud consumer are received. The requirements and cap are based on a sustainability target, published by the cloud consumer, and subscribed by cloud service providers. A list of cloud resources that satisfy the requirements are sent. Carbon emission values of the cloud resources at different load levels of the workload are sent. Based on a service level agreement requirement, a criticality level, and a peak load duration of the workload, and previous success rates of satisfying cloud resource requirements by cloud service providers, an optimized configuration of cloud resource(s) and cloud service provider(s) is selected. The cloud resource(s) are selected from the list and have a carbon footprint that does not exceed the cap at a given load level.

Carbon footprint-based control of cloud resource consumption

An embodiment includes identifying a robot that is available to perform a task involving an asset in a data center. The embodiment transmits a script to the robot that includes commands corresponding to actions to be executed by the robot in performing the task and transmits a dispatch command to the robot configured to enable the robot to travel from a robot resting place to the asset to begin the task. The embodiment relays a verification request received from the robot after the robot has completed a portion of the task to the vendor. The embodiment also relays, responsive to the verification request, an updated script based on updated instructions from the vendor for dynamically modifying the task to correcting an irregularity. The embodiment transmits, responsive to completion of the task, a recall command to the robot configured to enable the robot to travel from the asset to the robot resting place.

Managing shared robots in a data center

Social media representation and collaboration platform for enterprise IT

Systems, methods, and computer programming products for self-learning order dressing rules applied to manufacturing products in accordance with received product specifications. The translation from commercial characteristics to manufacturing characteristics of the product being manufactured are learned and adjusted to meet the specifications for quality required by the provided commercial characteristics. Reinforcement learning models learn from the quality characteristics of produced products by applying positive scores when the commercial to manufacturing characteristic translation is on-specification, otherwise a penalty is applied when an off-spec product is produced. Digital twins of manufacturing equipment, simulated in real time, provide insight and recommendations for achieving correct quality characteristics. Sensors in each device or within the surrounding environment help digital twins to measure operational performance and lifecycle of the manufacturing equipment against historical baselines. Reinforcement models dynamically adjust equipment settings for producing products to account for equipment performance degradation over time and changes in operation performance.

Self-learning manufacturing using digital twins

In an approach to improve multi-data center environments by minimizing the environmental impact of workloads in multi-data center environments embodiments migrate at least a portion of one or more workloads between one or more data centers automatically to maximize a usage of renewable energy based on a predetermined threshold score of input power and a combination of renewal energy sources. Further, embodiments dictate, by a policy engine, where at least a portion of the one or more workloads can be hosted. Additionally, embodiments control, by a scheduling engine, how, when, and where at least a portion of the one or more workloads will migrate, and perform data replication to migrate data between a plurality of data center locations.

Minimizing the environmental impact of workloads

An embodiment of the invention may include a method, computer program product and system for deployment of microservices within a shared pool of configurable computing resources. An embodiment may include creating a dependency map for a plurality of microservices of an application deployed on the shared pool of configurable computing resources. An embodiment may include identifying attributes, with associated values, for each microservice of the plurality of microservices and identifying eligible deployment locations within the shared pool of configurable computing resources. An embodiment may include creating a bipartite graph based on the plurality of microservices and the identified eligible deployment locations. An embodiment may include applying bipartite matching to the shared pool of configurable computing resources based on the created bipartite graph. An embodiment may include, based on the applied bipartite matching, relocating one or more microservices within the shared pool of configurable computing resources.

Microservice placement in hybrid multi-cloud using graph matching

A system and method is provided to selectively process and store tables of a relational database by calculating an overall data sensitivity score for each table based on predefined attribute rules; performing column-wise splitting of at least one of the tables into a first table and a second table based on the overall data sensitivity score of each table, thereby generating a total number of relational database tables; storing a first subset of the total number of relational database tables in a private cloud storage database in a distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables; and storing a second subset of the total number of relational database tables in a public cloud storage database of the distributed storage environment based on the overall data sensitivity scores of each of the total number of relational database tables.

Sensitivity-based database processing and distributed storage

Application-specific prioritization of streaming data replication. Data streamed from connected devices is selectively replicated to data storage clusters based on needs of the applications being served by the data. Data characterization supports prioritized replication processing. Statistical metrics compare streaming data with estimated values to characterize the data for prioritization.

Value-based replication of streaming data

Provisioning a virtual subject matter expert (SME) can include hosting the virtual SME on computer hardware communicatively coupled with a data communications network. The virtual SME can comprise a set of actions that are configured by a technology vendor. As configured, the actions can support a technology that is supplied to a client by the technology vendor. Access to the virtual SME can be provided on a provisioning platform communicatively coupled to the data communications network. The virtual SME can be provisioned to the client in response to a request from the client conveyed via the data communications network. The virtual SME can be provisioned to the client by operatively coupling the virtual SME with an enterprise information technology domain that includes the technology supplied to the client by the technology vendor.

Virtual subject matter expert provisioning

Monitoring and enhancing performance of containers using a calibration technique is implemented using a computer. Performance of a new container as part of an application running on the computer is checked by comparing a current performance of the new container with baseline data corresponding to the new container. The baseline data is derived from a calibration container corresponding to the new container. The new container is categorized in a category of performance based on the checking of the performance of the new container. An alert can be sent to a device of an administrator regarding the new container meeting a threshold of performance, in response to the new container meeting the threshold of performance. The alert can be sent to the device of the administrator for the administrator to initiate an action pertaining to the new container in response to receiving the alert.

Calibration technique using computer analysis for ascertaining performance of containers

Managing hybrid cloud resources by grouping at least a portion of the elements of a data set according to attribute sensitivity into a cluster of elements, computing a resource allocation impact of the cluster of elements, computing an information gain associated with the set of elements, and allocating cloud resources according to the resource allocation impact and information gain.

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Bengaluru, India

Pramod Vadayadiyil Raveendran