Abstract: A method, computer program product, and computer system for dynamically allocating shared memory among multiple execution threads via use of a dynamically changing memory threshold. A trained machine learning model (MLM) is executed to calculate the memory threshold (MTH) using values of one or more input parameters as input to the MLM. After executing the MLM, a request is received from a requesting execution thread for a requested amount (MR) of the shared memory. In response to the request, the shared memory is distributed among one or more current execution threads currently using the shared memory and the requesting execution thread. The redistributing is performed as a function MTH, MR, MU, and MC, wherein MU is a total amount of the shared memory currently being used by the one or more current execution threads, and wherein MC is a memory capacity of the shared memory.

Abstract: Methods and systems for provisioning resources includes classifying a request for a virtual server instance (VSI) according to resources specified in the request. Dependent resources that the VSI will use are predicted based on the specified resources. A provisioning plan is generated, including timing and configuration information, based on the specified resources and the dependent resources. Resources for the VSI are provisioned according to the provisioning plan, including allocating hardware resources on a computing system.

Abstract: Shutdown intervals between nodes of a dependency tree are determined. The dependency tree represents a system to be shut down and the nodes represent system components of the system. The determining the shutdown intervals includes calculating, for a pair of nodes of the dependency tree, a shutdown interval. The calculating is based on a relationship between the pair of nodes. The calculating is repeated for a plurality of pairs of nodes to obtain a plurality of shutdown intervals. The plurality of shutdown intervals is used in a shutdown of the system components. At least one system component represented by at least one node on a higher node level of the dependency tree is to be shut down prior to at least one other system component represented by at least one other node on a lower node level of the dependency tree.

Abstract: Embodiments of the present disclosure provide systems and methods for implementing self-bias detection based on performance and importance. A disclosed computer implemented method aggregates continuous input data through a K-means clustering algorithm to reduce the number of aggregated sub-group data pairs, enabling a reduced calculation time for computing bias and enhanced performance. The self-bias detection identifies a scale factor and a balance factor of aggregated sub-group data pairs, which indicate the importance of the detected bias.

Abstract: A method, system, and computer program product that is configured to: receive at least one workload of a mixed addressing mode application; classify the at least one workload with artificial intelligence (AI) including a support vector machine (SVM) algorithm; match at least one agent to the at least one workload based on a workload class and tuning policies; execute workload polices of the at least one workload based on the workload class and the tuning policies; evaluate a transaction per second (TPS) and response time of the at least one workload; calculate a reward of the at least one workload; and train a plurality of models based on historical data corresponding to the evaluated TPS, the evaluated response time, and the calculated reward.

Abstract: A method, computer system, and a computer program product for digital twin usage are provided. A first digital twin and performance data of the first digital twin are input into a first machine learning model to produce a second digital twin. The first machine learning model performs neural network-based data clustering. The first and second digital twins digitally represent a first physical entity. The second digital twin includes one or more changes from the first digital twin. Performance data of the second digital twin is analyzed.

Abstract: The method provides for enhancing transmission of data between multiple host arrays in separate cloud environments. Arrays of host devices are built, respectively, within a plurality of cloud environments. Data transmission from the data source is received by a first array of host devices of a first cloud environment, and the first array of host devices of the first cloud environment transmits the received data to a second array of host devices of a second cloud environment, and the first array of host devices and the second array of host devices transmit the data to a third array of host devices of a third cloud environment.

Abstract: Computer technology for use in a computer system including a database system including a database management sub-system, a primary database and a secondary database, with the secondary database being maintained at least approximately in synchronization with the primary database over time as changes are made to data of the primary database (for example, a database system that includes disaster recovery (DR) features). A statistics update is performed first on a standby database to determine whether query efficiency and/or query optimization really will be substantially improved by performance of the statistics update. If not, then statistics update on the primary database is postponed until a future time.

Abstract: In an approach for intelligent workload scheduling, a processor groups a plurality of batch jobs based on workload resource requests and dependencies of each batch job resulting in a plurality of groups. A processor schedules the plurality of batch jobs based on the plurality of groups. A processor monitors workload resource usage of system for running the plurality of batch jobs and a plurality of transaction workloads. A processor identifies one or more scheduled transaction workloads will not be able to be completed in under a preset time threshold. A processor reduces a resource quota of one or more batch jobs of the plurality of batch jobs based on type of resource that is needed for the one or more scheduled transaction workloads.

Abstract: A method and system for registering images as non-fungible token images include a non-fungible token verifier verifying that an image has not been registered as a registered non-fungible token. Once the image is verified, an object recognizer recognizes objects of the image that are classified into a object queue. A characteristic extractor then extracts one or more characteristics of the queued objects. A non-fungible token denormalizer then denormalizes the image into one or more denormalized images each having denormalization attributes. A uniqueness purifier then inspects objects of each of the denormalized images for characteristics extracted from objects in the queue, where matching characteristics between the objects of the denormalized images and objects of the image define unique parameters. Non-fungible token register then registers the image as a unique non-fungible token image.

Abstract: According to one embodiment, a method, computer system, and computer program product for detecting, interpreting, and translating sign language is provided. The present invention may include responding to a detection of an active web conference, by analyzing a user's profile and profiles of an audience of the web conference; capturing gestures and spoken language of a presenter using one or more Internet of Things devices; processing the captured gestures and the spoken language of the presenter to interpret the gestures and the spoken language of the presenter; translating the gestures and the spoken language of the presenter into sign language; generating a digital avatar; and displaying the digital avatar along with dynamic sign language movements using the digital avatar.

Abstract: A computer-implemented method, a system, and a computer program product for service delivery are disclosed. In the method, a static analysis can be performed on a plurality of services to be deployed in an environment comprising a plurality of nodes, to determine dependency relationships among the plurality of services. The plurality of services can be deployed in the environment based on the dependency relationships, a predefined priority for each of the plurality of services, and deployment time for each of the plurality of services. Also, the plurality of services can be scheduled into corresponding nodes in the environment.

Abstract: A computer implemented method performs a task for a vehicle. A number of processor units detects a presence of the vehicle on a road. The number of processor units divides the task to be performed for the vehicle into subtasks. The number of processor units assigns the subtasks to Internet of Things nodes on possible paths that the vehicle can travel on the road. The number of processor units processes the subtasks using a set of the Internet of Things nodes on a selected path in the possible paths in real time as the vehicle moves on the selected path to form subtask results. The number of processor units combines the subtask results from the processed subtasks to form a task result for the task.

Abstract: Mechanisms are provided for personalizing a computer generated virtual environment. Sensors associated with a user collect emotion data representing physiological conditions of the user in response to stimuli. Source computing systems collect stimuli context data and the stimuli context data is correlated with the emotion data. Machine learning model(s) are trained, based on the emotion data and correlated stimuli context data, to predict an emotion of the user from patterns of input data. Runtime emotion data is received from the sensors, and runtime stimuli context data is received from a virtual environment provider computing system for a computer generated virtual environment. The trained machine learning model(s) generate a predicted emotion of the user based on the runtime emotion data and the runtime stimuli context data. In cases, the virtual environment is modified based on the predicted emotion of the user.

Abstract: According to one embodiment, a method, computer system, and computer program product for managing an event stream is provided. The embodiment may include identifying two or more endpoints, including at least one sender and at least one recipient. The embodiment may also include determining one or more exchange rules to govern a stream of messages sent from the at least one sender to the at least one recipient. The embodiment may further include generating a smart contract according to the one or more exchange rules. The embodiment may also include receiving an input message in the stream of messages from the at least one sender. The embodiment may further include in response to receiving the input message, sending an output message to the at least one recipient.

Abstract: Various embodiments are provided herein for implementing multiple thread scenes in a computing environment virtualized metaverse. A multi-layered thread to be instanced in the virtualized metaverse is developed. A main thread scene, and a virtualized avatar that is projected in the main thread scene, is developed. The creation and merger of the main thread scene with other thread scenes featuring the virtualized avatar throughout the multi-layered thread according to a predetermined methodology or overall theme, is enabled.

Abstract: A method, including: identifying static application features of an application; identifying resource access features of the application; labeling a translation lookaside buffer (TLB) miss threshold of a runtime feature of the application; determining utilization of larger pages during the runtime based on the TLB miss threshold; and setting the TLB miss threshold based on the determined utilization of the larger pages.

Abstract: A computer-implemented method for a service mesh to simulate and address a situation in which there are several transactions among services with an asynchronous relationship between the services is provided. The computer-implemented method includes identifying that the situation occurs with a response provided to a user upon a user invocation of one of the services, analyzing the situation to determine that the asynchronous relationship between the services caused an error in at least one of the several transactions, recording data of a next user invocation of the one of the services, modifying the data of the next user invocation of the one of the services to correct the error and to thereby generate modified data, simulating an execution of the next user invocation of the service using the modified data and confirming that the modified data corrects the error based on results of the simulating.

Abstract: Dynamic adjusting of pod resource limits is provided at runtime of a container orchestration platform pod. The process includes deploying a container orchestration platform pod with one or more pod resources in a computing environment, where the pod resources have one or more associated pod resource limits. Further, the process includes monitoring a runtime resource usage of the container orchestration platform pod, and predicting, by a trained machine learning model, upcoming resource usage of the container orchestration platform pod. The predicting uses, at least in part, the monitored runtime resource usage. Further, the process includes dynamically adjusting a pod resource limit of the one or more pod resource limits of the container orchestration platform pod in the computing environment. The dynamically adjusting is based on the monitored runtime resource usage, and the predicted upcoming resource usage.

Abstract: Embodiments of the present disclosure relate to a method, system, and computer program product for comparison of database data. According to the method, a first tree structure corresponding to first data segments of first database data and a second tree structure corresponding to second data segments of second database data are at least partially obtained. Each node of the first or second tree structure indicating a characteristic value of at least one of the first or second data segments, and nodes of the first or second tree structure are divided into a first or second plurality of branches from a first or second root node based on update frequencies of the first or second data segments. A difference between the first data segments and the second data segments is determined by at least comparing characteristic values indicated by nodes in the obtained parts of the first and second tree structures.