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 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 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 for inferring an application type, based on an operational characteristic I/O pattern of a storage volume. One or more processors determine at least one operational characteristic of each storage volume of a storage volume group associated with an application. One or more labels are assigned for each storage volume, based on the operational characteristics of each storage volume. At least one template is received that includes labels of storage volume characteristics of known application types. One or more processors infer a type of application associated with the storage volume group, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

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 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: Operational characteristic I/O patterns of each storage volume of a storage volume group, and at least one operational characteristic of each storage volume associated with an application, are determined. Unsupervised learning is used to form clusters of storage volumes of similar characteristics. Labels are generated, assigned, and aggregated for each characteristic of each storage volume. Templates are received that includes labels of storage volume characteristics of known application types. An application type associated with the storage volume group is inferred, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

Abstract: Operational characteristic I/O patterns of each storage volume of a storage volume group, and at least one operational characteristic of each storage volume associated with an application, are determined. Unsupervised learning is used to form clusters of storage volumes of similar characteristics. Labels are generated, assigned, and aggregated for each characteristic of each storage volume. Templates are received that includes labels of storage volume characteristics of known application types. An application type associated with the storage volume group is inferred, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

Abstract: An approach for reactive throttling of heterogeneous migration sessions running concurrently in a virtualized cloud environment is disclosed. The heterogeneous migration sessions are characterized by several attributes such as the type of function performed by the migration session and the value that is attained by performing the migration session. An input/output (I/O) path to any resource in the virtualized cloud environment computing that is experiencing excessive resource congestion is identified. Bottlenecks along the I/O path are then determined. Virtual-to-physical resource mapping information of resources in the virtualized cloud computing environment are used to obtain a list of the heterogeneous migration sessions contributing to the bottlenecks along the identified I/O path.

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: Storage area network (SAN) components contain a processor configured to provide a first event handling module that can receive an error event at a first hardware component of the storage area network. A database is accessed that contains associations between error event data and potential sources of errors. A plurality of hardware components are identified using the database and error event data. The hardware components are ranked and one is selected based on the ranking. An error notification is sent to a second event handling module of the hardware component. Based upon the response of the second event handling module, an error handling procedure is carried out.

Abstract: A method and system for intelligent tiering is provided. The method includes receiving a request for enabling a tiering process with respect to data. The computer processor retrieves a migration list indicating migration engines associated with the data. Additionally, an entity list of migration entities is retrieved and each migration entity is compared to associated policy conditions. In response, it is determined if matches exist between the migration entities and the associated policy conditions and a consolidated entity list is generated.

Abstract: A method for inferring an application type, based on an operational characteristic I/O pattern of a storage volume. One or more processors determine at least one operational characteristic of each storage volume of a storage volume group associated with an application. One or more labels are assigned for each storage volume, based on the operational characteristics of each storage volume. At least one template is received that includes labels of storage volume characteristics of known application types. One or more processors infer a type of application associated with the storage volume group, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

Abstract: A method and system. In response to a request for a proposed additional data transfer from a source storage system to a target storage system, an estimate is made of utilization of one or more data transfer components of the source storage system and the target storage system that would occur during the proposed additional data transfer combined with other scheduled data transfers involving the source storage system and the target storage system, based in part on (i) an I/O request rate for recent actual data transfers involving the source storage system and the target storage system combined with (ii) an estimated I/O request rate for the proposed additional data transfer. It is ascertained whether the estimated utilization exceeds a corresponding utilization threshold. If so, execution of the proposed additional data transfer is postponed. If not, execution of the proposed additional data transfer is initiated.

Abstract: Embodiments relate to impact-based migration scheduling. An aspect includes identifying, by a migration scheduling module, a plurality of feasible destination storage devices for a migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session during the migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session after the migration session is completed. Another aspect includes selecting a destination storage device from the identified destination storage devices based on the impact of the migration session during the migration session and the impact of the migration session after the migration session is completed. Another aspect includes determining a start time and transfer rate for the migration session based on the selected destination storage device.

Abstract: Operational characteristic I/O patterns of each storage volume of a storage volume group, and at least one operational characteristic of each storage volume associated with an application, are determined. Unsupervised learning is used to form clusters of storage volumes of similar characteristics. Labels are generated, assigned, and aggregated for each characteristic of each storage volume. Templates are received that includes labels of storage volume characteristics of known application types. An application type associated with the storage volume group is inferred, based on a best-fit match of the aggregate labels of the storage volumes of the storage volume group to the labels included in the templates of storage volume characteristics of known application types.

Abstract: A method and system for intelligent tiering is provided. The method includes receiving a request for enabling a tiering process with respect to data. The computer processor retrieves a migration list indicating migration engines associated with the data. Additionally, an entity list of migration entities is retrieved and each migration entity is compared to associated policy conditions. In response, it is determined if matches exist between the migration entities and the associated policy conditions and a consolidated entity list is generated.

Abstract: A method and system for intelligent tiering is provided. The method includes receiving a request for enabling a tiering process with respect to data. The computer processor retrieves a migration list indicating migration engines associated with the data. Additionally, an entity list of migration entities is retrieved and each migration entity is compared to associated policy conditions. In response, it is determined if matches exist between the migration entities and the associated policy conditions and a consolidated entity list is generated.

Abstract: Embodiments relate to impact-based migration scheduling. An aspect includes identifying, by a migration scheduling module, a plurality of feasible destination storage devices for a migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session during the migration session. Another aspect includes determining, for each of the identified destination storage devices, an impact of the migration session after the migration session is completed. Another aspect includes selecting a destination storage device from the identified destination storage devices based on the impact of the migration session during the migration session and the impact of the migration session after the migration session is completed. Another aspect includes determining a start time and transfer rate for the migration session based on the selected destination storage device.