Abstract: A computer-implemented method, according to one embodiment, includes receiving, by a server from a client application, a read request for a data block. The method further includes, reading, by the server, data of the data block and checksums associated with the data block, from a data storage drive, and storing, by the server, the data of the data block and the associated checksums in a buffer. A client node is caused, by the server, to use the associated checksums to perform a validity check for the data of the data block and a transmission from the server to the client node. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a server to cause the server to perform the foregoing method.

Abstract: A computer-implemented method, according to one embodiment, includes causing a memory assessment thread to perform a first predetermined node memory contention state determination sequence for a predetermined first node. The first predetermined node memory contention state determination sequence includes recording a first timestamp, and allocating dedicated memory of the predetermined first node. The first predetermined node memory contention state determination sequence furthermore includes attempting to fill the allocated memory with content subsequent to recording the first timestamp, and recording a second timestamp after attempting to fill the allocated memory. The method further includes determining a first memory contention state of the predetermined first node based on results of performing the first predetermined node memory contention state determination sequence. The determined first memory contention state is caused to be included in a first type of message sent to a second node.

Abstract: Computer technology for use with a computer data storage system including a plurality of client computers and a plurality of collector nodes. A file consists of data blocks with each block resides on one of the persistent storage device. Data received at client computers are buffered and then transferred to selected collector nodes depending on which data blocks the data belong to. Location-aware collector node selection is performed by selecting collector nodes, from the plurality of collector nodes, by querying a file system allocation subsystem to determine selected collector nodes for the given data block that owns a persistent storage device on which the given block is allocated. Data for each data block is then sent to and stored in its selected collector nodes.

Abstract: A computer-implemented method, according to one embodiment, includes receiving, by a server from a client application, a read request for a data block. The method further includes, reading, by the server, data of the data block and checksums associated with the data block, from a data storage drive, and storing, by the server, the data of the data block and the associated checksums in a buffer. A client node is caused, by the server, to use the associated checksums to perform a validity check for the data of the data block and a transmission from the server to the client node. A computer program product, according to another embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a server to cause the server to perform the foregoing method.

Abstract: Computer technology for use with a computer data storage system including a plurality of client computers and a plurality of collector nodes. A file consists of data blocks with each block resides on one of the persistent storage device. Data received at client computers are buffered and then transferred to selected collector nodes depending on which data blocks the data belong to. Location-aware collector node selection is performed by selecting collector nodes, from the plurality of collector nodes, by querying a file system allocation subsystem to determine selected collector nodes for the given data block that owns a persistent storage device on which the given block is allocated. Data for each data block is then sent to and stored in its selected collector nodes.

Abstract: A method and a computer system for asymmetric replication of data are provided. Storage of a set of data is organized as a first copy and as a second copy in non-volatile storage. The second copy is reliable and stored so as to be readable at a speed slower than for the first copy. A read instruction regarding the set of data is received and performed preferentially via the first copy such that the asymmetric replication achieves enhanced performance speed. A request to execute a write operation is received. The write operation is executed to the first copy and to the second copy. In response to determining that the write operation to the first copy was unsuccessful, a label for the first copy is set as stale.

Abstract: A method and a computer system for asymmetric replication of data are provided. Storage of a set of data is organized as a first copy and as a second copy in non-volatile storage. The second copy is reliable and stored so as to be readable at a speed slower than for the first copy. A read instruction regarding the set of data is received and performed preferentially via the first copy such that the asymmetric replication achieves enhanced performance speed. A request to execute a write operation is received. The write operation is executed to the first copy and to the second copy. In response to determining that the write operation to the first copy was unsuccessful, a label for the first copy is set as stale.

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: A system for improving workload performance using data locality and workload placement may include a schedule module that schedules workloads for execution on a computing device in one or more computing devices. Also, the computing devices access a shared data storage. Further, the system may include a tracking module that maintains a data store that associates paths in the shared data storage with dispatched workloads and associates the paths with at least one computing device, wherein a dispatched workload and the at least one computing device accessed a path; and a preferred device identification module that identifies preferred computing devices in the one or more computing devices for executing undispatched workloads based on information in the data store, wherein the schedule module schedules the execution of the undispatched workloads in response to the identified preferred computing devices.

Abstract: A method, information processing system, and computer program product are provided for managing operating system interference on applications in a parallel processing system. A mapping of hardware multi-threading threads to at least one processing core is determined, and first and second sets of logical processors of the at least one processing core are determined. The first set includes at least one of the logical processors of the at least one processing core, and the second set includes at least one of a remainder of the logical processors of the at least one processing core. A processor schedules application tasks only on the logical processors of the first set of logical processors of the at least one processing core. Operating system interference events are scheduled only on the logical processors of the second set of logical processors of the at least one processing core.

Abstract: A system for improving workload performance using data locality and workload placement may include a schedule module that schedules workloads for execution on a computing device in one or more computing devices. Also, the computing devices access a shared data storage. Further, the system may include a tracking module that maintains a data store that associates paths in the shared data storage with dispatched workloads and associates the paths with at least one computing device, wherein a dispatched workload and the at least one computing device accessed a path; and a preferred device identification module that identifies preferred computing devices in the one or more computing devices for executing undispatched workloads based on information in the data store, wherein the schedule module schedules the execution of the undispatched workloads in response to the identified preferred computing devices.

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: Various embodiments monitor system noise in a parallel computing system. In one embodiment, at least one set of system noise data is stored in a shared buffer during a first computation interval. The set of system noise data is detected during the first computation interval and is associated with at least one parallel thread in a plurality of parallel threads. Each thread in the plurality of parallel threads is a thread of a program. The set of system noise data is filtered during a second computation interval based on at least one filtering condition creating a filtered set of system noise data. The filtered set of system noise data is then stored.

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: Software and resources, pre-deployed in accordance with a disaster recovery policy, are placed in a standby state to provide rapid disaster recovery in a cloud storage environment. When a disaster recovery situation is detected, (for example a loss of access to primary data), the pre-deployed resources are immediately activated according to the disaster recovery policy to provision and re-configure adequate resources and rapidly restore access to the data (such as restoring data from cloud backup storage). A disaster recovery situation may involve, for example, loss of hardware availability, network bandwidth interruption, or a sudden and large unforeseen jump in storage retrieval request volume (for example a recall storm).

Abstract: A method, information processing system, and computer program product are provided for managing operating system interference on applications in a parallel processing system. A mapping of hardware multi-threading threads to at least one processing core is determined, and first and second sets of logical processors of the at least one processing core are determined. The first set includes at least one of the logical processors of the at least one processing core, and the second set includes at least one of a remainder of the logical processors of the at least one processing core. A processor schedules application tasks only on the logical processors of the first set of logical processors of the at least one processing core. Operating system interference events are scheduled only on the logical processors of the second set of logical processors of the at least one processing core.