Abstract: Provided are a method, system and program for publishing configuration patterns for storage resources and storage performance models from client systems to share with client systems in a network computing environment. A determination is made of a configuration pattern of storage resources within the client computing environment and storage performance metrics for the configuration pattern of storage resources within the client. At least one storage performance model is created based on the determined storage performance metrics. Information on the at least one storage performance model and the configuration pattern of the storage resources for which the storage performance metrics were determined are published to the service provider.

Abstract: A method and associated systems for optimized orchestration of a data-migration project. A data-migration orchestration system represents a hierarchical organization of each dataset to be migrated as a tree, where each leaf node of the tree represents data to be migrated and where a path between the leaf node and the root node represents a hierarchical directory pathname of sensitive data represented by the leaf node. Each tree is assigned a sensitivity signature that is proportional to the relative sensitivity and access frequency of the dataset represented by that tree. The signatures are organized into clusters as a function of the distances between each signature, and each signature is associated with a soft migration cost specific to that signature's cluster. A soft cost for migrating an application that requires multiple datasets may be determined by adding the migration costs associated with each of the multiple datasets.

Abstract: Systems and methods for resource provisioning are disclosed. A method includes: generating, by a computer device, a resource provisioning policy for a resource; receiving, by the computer device, a request for an allocation of the resource from an account; applying, by the computer device, the resource provisioning policy to the request based on receiving the request; automatically approving or denying, by the computer device, the request based on the applying the resource provisioning policy to the request; updating, by the computer device, the resource provisioning policy for the account based on the automatic approving or denying the request.

Abstract: One embodiment provides a method comprising receiving metadata comprising univariate time series data for each variable of a multivariate time series. The method comprises, for each variable of the multivariate time series, applying a hybrid and hierarchical model selection process to select an anomaly detection model suitable for the variable based on corresponding univariate time series data for the variable and covariations and interactions between the variable and at least one other variable of the multivariate time series, and detecting an anomaly on the variable utilizing the anomaly detection model selected for the variable. Based on each anomaly detection model selected for each variable of the multivariate time series, the method further comprises performing ensemble learning to determine whether the multivariate time series is anomalous at a particular time point.

Abstract: One embodiment provides a method comprising receiving metadata comprising univariate time series data for each variable of a multivariate time series. The method comprises, for each variable of the multivariate time series, applying a hybrid and hierarchical model selection process to select an anomaly detection model suitable for the variable based on corresponding univariate time series data for the variable and covariations and interactions between the variable and at least one other variable of the multivariate time series, and detecting an anomaly on the variable utilizing the anomaly detection model selected for the variable. Based on each anomaly detection model selected for each variable of the multivariate time series, the method further comprises performing ensemble learning to determine whether the multivariate time series is anomalous at a particular time point.

Abstract: A method and associated systems for a workload-aware thin-provisioning system that allocates physical storage to virtual resources from pools of physical storage volumes. The system receives constraints that limit the amount of storage that can be allocated from each pool and the total workload that can be directed to each pool. It also receives lists of previous workloads and allocations associated with each volume at specific times in the past. The system then predicts future workloads and allocation requirements for each volume by regressing linear equations derived from the received data. If the predicted values indicate that a pool will at a future time violate a received constraint, the system computes the minimum costs to move each volume of the offending pool to a less-burdened pool. It then selects the lowest-cost combination of volume and destination pool and then moves the selected volume to the selected pool.

Abstract: Provided are a computer program product, method, and system for providing information on published configuration patterns of storage resources to client systems in a network computing environment. Published configuration patterns of storage resources and storage performance models for the published configuration patterns are received from the client systems and stored in a catalog. The published storage performance models are based on storage performance and workloads realized at the configuration patterns of the storage resources. A determination is made from the catalog of at least one published configuration pattern that is similar to a requested configuration pattern requested by a client system according to at least one storage attribute. A result set is generated indicating the determined at least one published configuration pattern and the storage performance model and sent to the requesting client system.

Abstract: A method and associated systems for a workload-aware thin-provisioning system that allocates physical storage to virtual resources from pools of physical storage volumes. The system receives constraints that limit the amount of storage that can be allocated from each pool and the total workload that can be directed to each pool. It also receives lists of previous workloads and allocations associated with each volume at specific times in the past. The system then predicts future workloads and allocation requirements for each volume by regressing linear equations derived from the received data. If the predicted values indicate that a pool will at a future time violate a received constraint, the system computes the minimum costs to move each volume of the offending pool to a less-burdened pool. It then selects the lowest-cost combination of volume and destination pool and then moves the selected volume to the selected pool.

Abstract: Systems and methods for resource provisioning are disclosed. A method includes: generating, by a computer device, a resource provisioning policy for a resource; receiving, by the computer device, a request for an allocation of the resource from an account; applying, by the computer device, the resource provisioning policy to the request based on receiving the request; automatically approving or denying, by the computer device, the request based on the applying the resource provisioning policy to the request; updating, by the computer device, the resource provisioning policy for the account based on the automatic approving or denying the request.

Abstract: In one embodiment, a method includes determining a first set of similarity metrics for a first storage environment, the first set of similarity metrics being based on predetermined parameters related to the first storage environment, at least one of the predetermined parameters being relating to a redundancy of connections and devices in the first storage environment. In addition, the method includes obtaining a second set of similarity metrics for a second storage environment. Also, the method includes determining that the second storage environment is similar to the first storage environment by calculating a similarity measurement score between the sets of similarity metrics for the first and second storage environments. Moreover, the method includes re-aligning the first storage environment according to one or more identified aspects of the second storage environment that improve performance of the first storage environment.

Abstract: A method, computer system, and computer program product to detect anomalies in a multivariate or multidimensional time series data set. The time series data set is retrieved from a monitored device. A pair of neural networks are trained simultaneously using the retrieved time series data set by implementing an adversarial training process, to generate a generative neural network and a discriminative neural network. The anomalies in the time series data set of the monitored device are detected by implementing one or both of the generative neural network and the discriminative neural network to monitor the time series data set.

Abstract: Provided are a computer program product, method, and system for providing information on published configuration patterns of storage resources to client systems in a network computing environment. Published configuration patterns of storage resources and storage performance models for the published configuration patterns are received from the client systems and stored in a catalog. The published storage performance models are based on storage performance and workloads realized at the configuration patterns of the storage resources. A determination is made from the catalog of at least one published configuration pattern that is similar to a requested configuration pattern requested by a client system according to at least one storage attribute. A result set is generated indicating the determined at least one published configuration pattern and the storage performance model and sent to the requesting client system.

Abstract: Embodiments relate to cross-domain service request placement in a software defined environment (SDE). An aspect includes receiving a service request corresponding to a job to be completed in the SDE. Another aspect includes determining a first computer device in a first domain, and a second computer device in a second domain, that are capable of performing the service request. Another aspect includes determining, for the first and second computer devices, first and second pluralities of available service classes. Another aspect includes determining, for the first and second computer devices, a first and second plurality of costs of performing the service request, wherein each of the first and second plurality of costs corresponds to a single respective service class. Yet another aspect includes selecting one of the first computer device and the second computer device to perform the service request based on the first and second plurality of costs.

Abstract: An aspect includes input/output (I/O) stack modeling. A processor determines a client configuration of a client I/O stack that includes layers with configurable parameters to control storage and retrieval of data between an uppermost layer and lowest layer. A model of the client I/O stack is built on a layer basis that defines input workload characteristics, output workload characteristics, and layer configuration parameters for the layers of the model based on the client configuration. Workload characteristics of the uppermost layer of the client I/O stack are fed to the model. The processor determines a statistical distribution of workload characteristics associated with each of the layers of the client I/O stack. I/O performance results are captured for layers of the model based on feeding the workload characteristics of the uppermost layer.

Abstract: A method for scheduling one or more backup jobs among backup clients with a common backup window. The method includes collecting and recording historical information pertaining to the backup clients with the common backup window. An application then estimates the number of open files during the common backup window based on the historical information and infers an impact of skipping a backup of the open files during the common backup window, where the impact is inferred from the historical information according to one or more predetermined criteria. The estimated number of open files and the inferred impact of skipping the backup of the estimated number of open files during the common backup window are then combined according to a predetermined cost function. The order of the backup jobs for the backup clients during the common backup window is then scheduled to reduce the predetermined cost function.

Abstract: A method and associated systems for a workload-aware thin-provisioning system that allocates physical storage to virtual resources from pools of physical storage volumes. The system receives constraints that limit the amount of storage that can be allocated from each pool and the total workload that can be directed to each pool. It also receives lists of previous workloads and allocations associated with each volume at specific times in the past. The system then predicts future workloads and allocation requirements for each volume by regressing linear equations derived from the received data. If the predicted values indicate that a pool will at a future time violate a received constraint, the system computes the minimum costs to move each volume of the offending pool to a less-burdened pool. It then selects the lowest-cost combination of volume and destination pool and then moves the selected volume to the selected pool.

Abstract: Protecting and restoring data within a networked (e.g. cloud) storage computing environment through asynchronous replication and remote backup of data and its associated metadata. Data backup and recovery functionality provides data backups by detecting incremental updates to the data and its associated metadata at specific points in time determined by policies. The policies are configurable based on user requirements. Multiple copies of the data backups can be made and stored in separate compressed files at backup/disaster recovery locations. The backups of data and its associated metadata, which includes file system configuration information can be used to restore the state of a computer file system to that of a given point-in-time. Accordingly, a data protection approach is disclosed for protecting data at both the file system level and application level.

Abstract: A method and associated systems for a workload-aware thin-provisioning system that allocates physical storage to virtual resources from pools of physical storage volumes. The system receives constraints that limit the amount of storage that can be allocated from each pool and the total workload that can be directed to each pool. It also receives lists of previous workloads and allocations associated with each volume at specific times in the past. The system then predicts future workloads and allocation requirements for each volume by regressing linear equations derived from the received data. If the predicted values indicate that a pool will at a future time violate a received constraint, the system computes the minimum costs to move each volume of the offending pool to a less-burdened pool. It then selects the lowest-cost combination of volume and destination pool and then moves the selected volume to the selected pool.

Abstract: Performing design optimization using an augmented reality system. Baseline data comprising baseline sensor data and baseline user input data is received by a computer system. An interactive baseline design optimization problem based on the baseline data is generated by the computer system. The baseline interactive optimization problem is transmitted by the computer system to the augmented reality system. Refined data comprising refined sensor data and refined user input data is received by the computer system. An interactive refined optimization problem based on the refined data and the baseline data is generated by the computer system. The interactive refined optimization problem is transmitted by the computer system to the augmented reality system.

Abstract: Performing design optimization using an augmented reality system. Baseline data comprising baseline sensor data and baseline user input data is received by a computer system. An interactive baseline design optimization problem based on the baseline data is generated by the computer system. The baseline interactive optimization problem is transmitted by the computer system to the augmented reality system. Refined data comprising refined sensor data and refined user input data is received by the computer system. An interactive refined optimization problem based on the refined data and the baseline data is generated by the computer system. The interactive refined optimization problem is transmitted by the computer system to the augmented reality system.