Abstract: Authenticated base digital document(s) are issued to client(s) by an issuing party, and aggregate digital document(s) are received. An aggregate digital document includes base digital document(s) and attachment(s). Authenticity of the aggregate digital document(s) is verified, resulting in authenticated aggregate digital document(s), which are stored and/or redistributed. Authentication challenge(s) are sent by a verifying party to a client requesting part or all of an aggregate digital document from the client be verified. The part or all of the aggregate digital document is received and authenticity and integrity are verified, resulting in an authenticated aggregate digital document. The client verifies authenticity of a base digital document and receives the authentication challenge(s) for an authenticated aggregate digital document and sends part or all of the authenticated aggregate digital document to the verifying party for verification by the verifying party.

Abstract: Authenticated base digital document(s) are issued to client(s) by an issuing party, and aggregate digital document(s) are received. An aggregate digital document includes base digital document(s) and attachment(s). Authenticity of the aggregate digital document(s) is verified, resulting in authenticated aggregate digital document(s), which are stored and/or redistributed. Authentication challenge(s) are sent by a verifying party to a client requesting part or all of an aggregate digital document from the client be verified. The part or all of the aggregate digital document is received and authenticity and integrity are verified, resulting in an authenticated aggregate digital document. The client verifies authenticity of a base digital document and receives the authentication challenge(s) for an authenticated aggregate digital document and sends part or all of the authenticated aggregate digital document to the verifying party for verification by the verifying party.

Abstract: Embodiments relate to a pool of persistent storage volumes. Capacity of the volumes is managed to ensure continued operation and function of the volumes with respect to their corresponding storage pool capacity threshold(s). One or more space savings techniques are selectively performed on a copy of a selected volume. Such techniques include measurement of capacity change and measurement of workload performance change. These measurements are leveraged to produce a subset of space reduction actions for execution. A space reduction action in the form of compression or thinning takes place on-demand on a corresponding volume.

Abstract: The embodiments described herein relate to dynamically managing metric data of a network environment with respect to a data storage system. A data retention policy is analyzed, which includes extracting one or more metric definitions from the retention policy. A relevance of a set of metric data is identified based on the analysis. The set of metric data includes an aggregation of one or more metric observations. A storage location in a data storage system for the set of metric values is selected based on the identified relevance. The data storage system includes a cache storage location and a persistent storage location. The set of metric data is retained in the selected storage location. As the retention policy is modified, select data may be re-classified and moved within the storage system based on the re-classification.

Abstract: Embodiments relate to a pool of persistent storage volumes. Capacity of the volumes is managed to ensure continued operation and function of the volumes with respect to their corresponding storage pool capacity threshold(s). One or more space savings techniques are selectively performed on a copy of a selected volume. Such techniques include measurement of capacity change and measurement of workload performance change. These measurements are leveraged to produce a subset of space reduction actions for execution. A space reduction action in the form of compression or thinning takes place on-demand on a corresponding volume.

Abstract: Embodiments relate to a pool of persistent storage volumes. Capacity of the volumes is managed to ensure continued operation and function of the volumes with respect to their corresponding storage pool capacity threshold(s). A background process groups the volumes within each pool into one or more clusters based on a similarity or commonality characteristic. The background process selectively performs one or more space savings techniques of a copy of a selected volume, measures impact data associated with the techniques, and maintains the data. At such time as the threshold level is approached for a storage pool, one or more space reduction actions take place on constituent volumes in view of the background process and the associated groups. The reduction action includes implementation of a space saving technique, such as compression and/or thinning, of one or more volumes in one or more clusters in an associated storage pool.

Abstract: Embodiments relate to a distributed map reduce network. An aspect includes receiving a description of a map reduce job comprising a time deadline. Another aspect includes selecting a set of candidate nodes from a plurality of nodes of the distributed map reduce network, wherein the set of candidate nodes are selected based on cost value. Another aspect includes determining an amount of time required to perform the map reduce job by the set of candidate nodes. Another aspect includes, based on the determined amount of time not meeting the time deadline: selecting a particular node of the set of candidate nodes for replacement based on the geographical location of the particular node; selecting a next node of the plurality of nodes based on the cost value of the next node; and replacing the particular node in the set of candidate nodes with the next node.

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: Data files can be backed up by copying, in response to a backup request, files from a client device to a backup archive and recording the status of the files. Transformed copies of files in the backup archive can be created by automatically applying a transform to the files received from the client device. Upon receiving a subsequent backup request, differences can be identified between transformed files in the backup archive and files on the client device, and in response to identified differences in the files, the transformed files can be copied back to the client 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: 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: The embodiments described herein relate to dynamically managing metric data of a network environment with respect to a data storage system. A data retention policy is analyzed, which includes extracting one or more metric definitions from the retention policy. A relevance of a set of metric data is identified based on the analysis. The set of metric data includes an aggregation of one or more metric observations. A storage location in a data storage system for the set of metric values is selected based on the identified relevance. The data storage system includes a cache storage location and a persistent storage location. The set of metric data is retained in the selected storage location. As the retention policy is modified, select data may be re-classified and moved within the storage system based on the re-classification.

Abstract: Methods, systems, and articles of manufacture for identifying compatible system configurations are provided herein. A method includes generating a second graph from a first graph of multiple devices in a network and a set of one or more network compatibility rules, wherein said generating comprises dividing each device in the first graph into multiple nodes in the second graph, and wherein each node in the second graph represents a valid configuration of a device in the first graph; identifying a sub-graph of two or more linked nodes in the second graph that is isomorphic to at least a portion of the first graph, wherein the two or more linked nodes in the second graph represent two or more configurations that are compatible based on the set of one or more network compatibility rules; and determining each of one or more changes needed to convert a current configuration in the network to a target configuration specified by the sub-graph.

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: Data files can be backed up by copying, in response to a backup request, files from a client device to a backup archive and recording the status of the files. Transformed copies of files in the backup archive can be created by automatically applying a transform to the files received from the client device. Upon receiving a subsequent backup request, differences can be identified between transformed files in the backup archive and files on the client device, and in response to identified differences in the files, the transformed files can be copied back to the client device.

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.