Abstract: Provided are techniques for model generation for determining optimal components of a storage subsystem based on measurements and models. Data for a storage subsystem having a plurality of components is collected, where the data comprises data for application resources, data for workload measurements, metadata, tags, and policies, and wherein the data comprises volume specific characteristics and storage functions. A plurality of models are generated based on the collected data, where each of the models includes a subset of the plurality of components of the storage subsystem. Characteristics for a new storage subsystem are received. The characteristics are matched to a model of the plurality of models. A recommendation of components of the matching model is created to create the new storage subsystem.

Abstract: 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.

Abstract: Optimizing non-functional requirements across components of a high availability cluster may include receiving metadata from each of a plurality of components of a high availability network cluster. The metadata is indicative of a value, measured by each of the plurality of components, of one or more non-functional requirements of a service level agreement (SLA) associated with an application. A predicted non-functional requirement is calculated using the metadata based on a model. The model is trained to determine the predicted non-functional requirement based on relationships between the metadata of each of the plurality of components. A potential violation of the SLA is determined based on a comparison of the predicted non-functional requirement and the metadata.

Abstract: 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.

Abstract: Collaborative machine capability enhancement is provided. It is determined whether an automated mobile machine is capable of performing an activity based on analysis of information corresponding to the activity and capabilities of the automated mobile machine. In response to determining that the automated mobile machine is incapable of performing the activity based on the analysis of the information corresponding to the activity and the capabilities of the automated mobile machine, a digital twin simulation associated with performing the activity is performed using the information corresponding to the activity and the capabilities of the automated mobile machine. An analysis of a result of the digital twin simulation associated with performing the activity is performed. A number of additional automated mobile machines needed to collaboratively perform the activity is determined based on the analysis of the result of the digital twin simulation.

Abstract: In one general embodiment, a computer-implemented method includes detecting individual sequential input/output (I/O) workloads in a stream of superimposed I/O workloads accessing a same physical volume. The detecting is based on a time dependency corresponding to accesses of blocks of the volume. In another general embodiment, a computer-implemented method includes detecting a plurality of sequential input/output (I/O) workloads in an I/O stream of superimposed workloads accessing a same volume, the detecting being based on a time dependency corresponding to accesses of blocks of the volume. A sequentiality factor is calculated for each of the sequential I/O workloads.

Abstract: In an approach for ensuring data protection and control in a distributed hybrid multi-cloud environment with Kubernetes clusters, a processor determines whether a respective quorum of the set of clusters are online. A processor, responsive to determining that a respective quorum of the set of clusters are online, determines whether one or more applications of the cluster are running on another cluster of the set of clusters. A processor, responsive to determining the one or more applications of the cluster are not running on another cluster of the set of clusters, determines whether the cluster is designated as a highest priority cluster. A processor, responsive to determining the cluster is designated as the highest priority cluster, determines whether a main cluster of the set of clusters is online. A processor, responsive to determining the main cluster is online, scales a new custom resource to one (1).

Abstract: In an approach for ensuring data protection and control in a distributed hybrid multi-cloud environment with Kubernetes clusters, a processor determines whether a respective quorum of the set of clusters are online. A processor, responsive to determining that a respective quorum of the set of clusters are online, determines whether one or more applications of the cluster are running on another cluster of the set of clusters. A processor, responsive to determining the one or more applications of the cluster are not running on another cluster of the set of clusters, determines whether the cluster is designated as a highest priority cluster. A processor, responsive to determining the cluster is designated as the highest priority cluster, determines whether a main cluster of the set of clusters is online. A processor, responsive to determining the main cluster is online, scales a new custom resource to one (1).

Abstract: Computer-implemented methods of training machine learning models and using the machine learning models for tailoring a multi-channel help desk environment. One or more computers train a machine learning model of selecting best attendance channels for respective customer clusters and for respective issue clusters. One or more computers train machine learning models of tailoring respective attendance channel types. One or more computers employ the machine learning models to determine a best attendance channel for resolving an information technology problem of a user and to predict channel tailoring characteristics for the best attendance channel. One or more computers employ genetic algorithm operators to determine a random attendance channel with random tailoring characteristics. One or more computer use random routing to route the user to one of the best attendance channel and the random attendance channel, avoiding undesired bias favorable toward the best attendance channel.