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.

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 capability is provided for predicting the performance of a communications network, such as a multi-stage communications network. The predicted performance includes, in one aspect, predicting the amount of bandwidth that a server of the communications network could deliver with a full load on the network. In one aspect, the predictive capability includes determining an aggregate load for each server of the network being analyzed; performing normalization on the loads of the links used by the servers to obtain updated link and/or server bandwidths; and determining for each server, a minimum bandwidth over the links in the server's route.

Abstract: A capability is provided for predicting the performance of a communications network, such as a multi-stage communications network. The predicted performance includes, in one aspect, predicting the amount of bandwidth that a server of the communications network could deliver with a full load on the network. In one aspect, the predictive capability includes determining an aggregate load for each server of the network being analyzed; performing normalization on the loads of the links used by the servers to obtain updated link and/or server bandwidths; and determining for each server, a minimum bandwidth over the links in the server's route.

Abstract: As disclosed herein, a method, executed by a computer, for analyzing a performance metric includes distributing link connection information for a network to each node in the network, pairing each node with another node in the network to provide paired nodes, testing a performance metric for the paired nodes, and generating a performance report for the network. The method may further include receiving link connection information for the network. The method may further include constructing a connectivity graph based on the received link connection information. The method can be used to test link performance within non-homogeneous networks. A computer system product and a computer program product corresponding to the method are also disclosed herein.

Abstract: As disclosed herein, a method, executed by a computer, for analyzing a performance metric includes distributing link connection information for a network to each node in the network, pairing each node with another node in the network to provide paired nodes, testing a performance metric for the paired nodes, and generating a performance report for the network. The method may further include receiving link connection information for the network. The method may further include constructing a connectivity graph based on the received link connection information. The method can be used to test link performance within non-homogeneous networks.