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: 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: 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: According to an aspect, document-based requirement identification and extraction includes parsing a set of documents and identifying relationships among parsed components of the documents and applying the parsed components and identified relationships to a meta-model that defines requirements. The requirements include a statement expressing a need and/or responsibility. A further aspect includes identifying candidate requirements and their candidate topics from results of the applying. For each of the identified candidate topics, a feature vector is built from the corresponding candidate requirements. A further aspect includes training the meta-model with the feature vectors, validating the meta-model, and classifying output of the validating to identify a subset of the candidate requirements, and corresponding topics expressed in the set of documents.

Abstract: A method, system and computer program product for detecting sensitive personal information in a storage device. A block delta list containing a list of changed blocks in the storage device is processed. After identifying the changed blocks from the block delta list, a search is performed on those identified changed blocks for sensitive personal information using a character scanning technique. After identifying a changed block deemed to contain sensitive personal information, the changed block is translated from the block level to the file level using a hierarchical reverse mapping technique. By only analyzing the changed blocks to determine if they contain sensitive personal information, a lesser quantity of blocks needs to be processed in order to detect sensitive personal information in the storage device in near real-time. In this manner, sensitive personal information is detected in the storage device using fewer computing resources in a shorter amount of time.

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: 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.

Abstract: Embodiments of the invention relate to recovering from a disaster associated with an information technology environment. An information technology environment is replicated to a service provider. A recovery plan is generated for the environment. The recovery plan includes two processes. In response to the service provider receiving a disaster recovery request associated with the environment, the service provider executes a disaster recovery protocol. The protocol includes simultaneously executes the first and second processes. The first process operates a workload in the form of one or more containers, and the second process is a background process that creates a replica of the environment. After completion of the replica creation, the workload is migrated to the replica.

Abstract: A method loads training samples and forms training data set from the training samples. The method uses the bidirectional LSTM recurrent neural network that includes one or more input cells and one or more output cells and trains it with the training data set. The method determines a sensitive information and confidence values based on analyzing a text with the trained neural network. The method selects predicted samples from the text, where the sensitive information confidence value corresponding to a one or more predicted samples is above a threshold value, based on determining that a sensitive information accuracy has improved. The method forms a new training data set, where the new training data set comprises the samples and the verified one or more predicted samples based on the verified one or more predicted samples, and trains the previously trained neural network with the new training data set.

Abstract: One embodiment provides a quality of service (QoS) monitoring framework for dynamically binding one or more customer applications to one or more microservices in a dynamic service environment, collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments, and modifying a monitoring infrastructure for the one or more customer applications based on the compliance data and the contextual data.

Abstract: A blockchain of transactions may be referenced for various purposes and may be later accessed by interested parties for ledger verification and information retrieval. One example method of operation may include one or more of monitoring a computing service via various nodes operating on a blockchain, identifying a proposed change to the computing service, storing details of the proposed change in a smart contract, storing the smart contract as a transaction in the blockchain, and validating the proposed change of the smart contract.

Abstract: A method for distributing data among storage devices. The method comprising one or more processors receiving a first graph workload that executes within a networked computing environment. The method further includes identifying data from the first graph workload that is utilized during the execution of the first graph workload that includes a plurality of data packets. The method further includes creating a first graph workload model representative of the graph structure of the first graph workload and determining two or more partitions that are representative of a distribution of the identified data utilized by the first graph workload based, at least in part, on the first graph workload model. The method further includes allocating a plurality of network accessible storage devices among the two or more partitions and copying a first set of data packets of the plurality of data packets to a network accessible storage device.

Abstract: A method and associated systems for identifying and correcting suboptimal storage-reclamation processes. A storage-management system uses information received in system-generated storage-reclamation reports to assign each user a set of reclamation scores. Each score identifies how effectively the user has been able to reclaim lost storage at particular times. These scores are organized into user-specific profiles that each consists of a chronological sequence of one user's scores. If a user's profile is “good” (that is, if the user's scores are consistently high) or “improving” (if scores are increasing over time), the system then determines whether that user's reclamation efforts have successfully reduced the amount of reclaimable storage controlled by the user. If not, the system infers that a suboptimal storage-reclamation process interfered with the user's reclamation efforts. The system then undertakes corrective action to identify and resolve the cause of the suboptimal process.

Abstract: A computer-implemented method according to one embodiment includes identifying a storage volume comprising a plurality of files, calculating a file level input/output operations per second (IOPS) value for each of a subset of the plurality of files within the storage volume, creating a predictive model for the storage volume, using metadata determined for the subset of the plurality of files and the IOPS values calculated for each of the subset of the plurality of files within the storage volume, estimating file level IOPS values for each of the plurality of files in the storage volume, utilizing the predictive model, combining the estimated and calculated file level IOPS values and comparing the combined values to a calculated volume level IOPS value for the storage volume, conditionally adjusting one or more of the estimated file level IOPS values, based on the comparing, and returning the estimated file level IOPS values.

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 for distributing data among storage devices. The method comprising one or more processors receiving a first graph workload that executes within a networked computing environment. The method further includes identifying data from the first graph workload that is utilized during the execution of the first graph workload that includes a plurality of data packets. The method further includes creating a first graph workload model representative of the graph structure of the first graph workload and determining two or more partitions that are representative of a distribution of the identified data utilized by the first graph workload based, at least in part, on the first graph workload model. The method further includes allocating a plurality of network accessible storage devices among the two or more partitions and copying a first set of data packets of the plurality of data packets to a network accessible storage device.