Abstract: Application transition and transformation is provided by observing and analyzing execution of a monolithic application to provide properties of the data communication of the monolithic application, identifying, by an artificial intelligence engine, atomic application element(s) having logical functional block(s) that function independent of other atomic application element, determining, by the artificial intelligence engine, candidate atomic application element(s) for migrating out of the monolithic application and refactoring, based on automated testing and validation, refactoring at least one candidate atomic application element into a respective at least one refactored element, where the refactoring selects a platform technology for the refactored element and implements functionality of the candidate atomic application element as the refactored element of the selected platform technology, and providing the at least one refactored element to system(s) for execution thereon.

Abstract: A processor determines the data to be replicated in the production site. The processor splits the data into a plurality of blocks. The processor determines one or more replication links. The processor determines an order of each one of the plurality of blocks to be sent over the one or more replication links, where the order of each one of the plurality of blocks to be sent over the one or more replication links is determined by a machine learning model.

Abstract: 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.

Abstract: A method includes: determining, by a computer device, affinities between components in a networked computer system; determining, by the computer device, a risk level of each of the components; determining, by the computer device, a risk level of the networked computer system; detecting, by the computer device, a threat in the networked computer system; determining, by the computer device, an action based on the threat, the risk level of ones of the components affected by the threat, and the risk level of the system; and reconfiguring, by the computer device, the networked computer system based on the determined action.

Abstract: A processor may identify one or more requirements based on a specific field. The processor may capture the one or more requirements. The one or more requirements may be based on a particular structure. The processor may orchestrate the one or more requirements based on respective requests of the one or more requirements. The processor may store the orchestration of the one or more requirements as respective artifacts.

Abstract: A method, computer program product, and a system where a processor(s) obtains, via a web interface, from a client, over a public network connection, a request to register for a financial transaction; a smart contract comprises the requested financial transaction. The processor(s) maps the client to a pre-defined security profile based on a security level associated with the security profile. The processor(s) selects, based on the security level, encryption keys from a repository and security codes. The processor(s) executes an encryption script, to automatically encrypt the contents of the smart contract utilizing the encryption keys and the security codes and by embedding SafeMath library codes in the smart contract.

Abstract: A transitioning process to integrate a computer-related service with one or more other computer-related services. The computer-related service and the one or more other computer-related services are analyzed to determine whether there is a conflict in integrating the computer-related service in the computing environment. A determination is made based on the analyzing whether one or more changes are to be made to a selected component. At least the analyzing and the determining are part of an automated process generated to integrate the computer-related service, and the automated process is at least a part of the transitioning process. An indication of a performance impact of executing at least the automated process to integrate the computer-related service is obtained. The transitioning process is to continue based on the performance impact meeting one or more selected criteria and based on determining that there are not one or more changes to be made to the selected component.

Abstract: A processor determines the data to be replicated in the production site. The processor splits the data into a plurality of blocks. The processor determines one or more replication links. The processor determines an order of each one of the plurality of blocks to be sent over the one or more replication links, where the order of each one of the plurality of blocks to be sent over the one or more replication links is determined by a machine learning model.

Abstract: A system for determining the optimal backup and restore location for lightweight applications is provided. A computer device identifies a set of training data, wherein the training data identifies a lightweight container, a corresponding backup lightweight container, and an optimization score for the lightweight container and the corresponding backup lightweight container. The computing device trains a machine learning model utilizing the identified training data. The computing device identifies a new lightweight container for backup. The computing device determines an optimal backup lightweight container for the new lightweight container utilizing the trained machine learning model.

Abstract: A method, computer program product, and a system where a processor(s) obtains, via a web interface, from a client, over a public network connection, a request to register for a financial transaction; a smart contract comprises the requested financial transaction. The processor(s) maps the client to a pre-defined security profile based on a security level associated with the security profile. The processor(s) selects, based on the security level, encryption keys from a repository and security codes. The processor(s) executes an encryption script, to automatically encrypt the contents of the smart contract utilizing the encryption keys and the security codes and by embedding SafeMath library codes in the smart contract.

Abstract: A system and method for triggering a recovery action during a component disruption in a production environment includes receiving data inputs from a plurality of components comprising the production environment, detecting an abnormality associated with a component of the plurality of components by analyzing the data inputs, and in response to the detecting the abnormality, triggering an analysis of the abnormality, analyzing the abnormality using a plurality of artificial intelligence engines to determine a plurality of outcomes from each artificial engine, consolidating the plurality of outcomes into a single outcome, the single outcome being input into an annotator engine to determine the recovery action to take during the component disruption in the production environment, and implementing the recovery action specific to the component with the detected abnormality.

Abstract: Systems and methods are provided for cognitive scheduling. A computer-implemented cognitive scheduling method includes receiving a plurality of computer executable tasks. The cognitive scheduling method also determines environmental parameters for executing each of the plurality of computer executable tasks. Additionally, the cognitive scheduling method generates a risk assessment for the plurality of computer executable tasks based on the environmental parameters of the respective computer executable tasks compared against a set of annotation criteria. A time slot in a scheduling list is assigning to each of the plurality of computer executable tasks based on the risk assessment. Based on the scheduling list, each of the plurality of computer executable tasks is executed in chronological order.

Abstract: A method and system for improving hardware and software resource optimization technology is provided. The method includes retrieving and analyzing operational parameters of hardware and software systems. In response, a probability of impact with respect to modified sizing requirements associated with the hardware and software systems is determined and actions including logical rules mapped to the operational parameters is determined. The actions are executed and an actual impact with respect to executing the modified sizing requirements is determined. In response, operational allocations of the hardware and software systems are modified with respect to operational functionality of the hardware and software systems.

Abstract: A method includes: determining, by a computer device, affinities between components in a networked computer system; determining, by the computer device, a risk level of each of the components; determining, by the computer device, a risk level of the networked computer system; detecting, by the computer device, a threat in the networked computer system; determining, by the computer device, an action based on the threat, the risk level of ones of the components affected by the threat, and the risk level of the system; and reconfiguring, by the computer device, the networked computer system based on the determined action.

Abstract: A method and system for improving hardware and software resource optimization technology is provided. The method includes retrieving and analyzing operational parameters of hardware and software systems. In response, a probability of impact with respect to modified sizing requirements associated with the hardware and software systems is determined and actions including logical rules mapped to the operational parameters is determined. The actions are executed and an actual impact with respect to executing the modified sizing requirements is determined. In response, operational allocations of the hardware and software systems are modified with respect to operational functionality of the hardware and software systems.

Abstract: A system and method for triggering a recovery action during a component disruption in a production environment includes receiving data inputs from a plurality of components comprising the production environment, detecting an abnormality associated with a component of the plurality of components by analyzing the data inputs, and in response to the detecting the abnormality, triggering an analysis of the abnormality, analyzing the abnormality using a plurality of artificial intelligence engines to determine a plurality of outcomes from each artificial engine, consolidating the plurality of outcomes into a single outcome, the single outcome being input into an annotator engine to determine the recovery action to take during the component disruption in the production environment, and implementing the recovery action specific to the component with the detected abnormality.