Abstract: A method and system for creating a new restore container. A count that is determined for each unique infrastructure container of existing unique infrastructure containers is a number of instances in which a particular container type that is unique for each unique infrastructure container has been utilized. In response to both determining that the count for one unique infrastructure container is equal to or greater than a specified threshold and determining that a popular infrastructure container does not exist within a central repository that stores the existing unique infrastructure containers, the new restore container is created within the central repository by determining a most similar container of the existing restore containers whose software stack content matches, in part, software content in a first software stack of the popular infrastructure container more closely than does software content in the software stack of any other existing restore container.

Abstract: An approach for managing replicated data is presented. A current usage of resources in a system and a threshold usage of the resources are determined. Based on inter-replica correlation(s) and inter-data correlation(s) specified by a unified replication metadata model, a proper subset of replicas included in a plurality of replicas is indexed by (i) if the current usage is less than the threshold usage, determining an expected additional resource usage due to performing an indexing task online and based on the expected additional resource usage, determining a resource affinity score for performing the indexing task online, or (ii) if the current usage is greater than or equal to the threshold usage, determining an expected resource usage due to performing the indexing task offline and based on the expected resource usage, determining a resource affinity score for performing the indexing task offline.

Abstract: A method and associated system for continuously optimizing data archive management scheduling. A flow network is modeled, which creates vertexes organized in multiple levels and creating multiple edges sequentially connecting the vertexes of the multiple levels. The multiple levels consist of N+1 levels denoted as LEVEL0, LEVEL1, . . . , LEVELN, wherein N is at least 2, wherein LEVEL0 includes a source vertex, wherein LEVEL1 includes at least one task vertex associated with at least one archive management task, wherein EDGEK is directed from the vertex of LEVELK?1 to the vertex of LEVELK for K=1, . . . , N, wherein EDGE1 indicates that a first task associated with a first task vertex of LEVEL1 is scheduled by a job scheduler, and wherein EDGE1 has a first weight equal to a time unit to perform the first task.

Abstract: A method and system for creating a new restore container. A count that is determined for each unique infrastructure container of existing unique infrastructure containers is a number of instances in which a particular container type that is unique for each unique infrastructure container has been utilized. In response to both determining that the count for one unique infrastructure container is equal to or greater than a specified threshold and determining that a popular infrastructure container does not exist within a central repository that stores the existing unique infrastructure containers, the new restore container is created within the central repository by determining a most similar container of the existing restore containers whose software stack content matches, in part, software content in a first software stack of the popular infrastructure container more closely than does software content in the software stack of any other existing restore container.

Abstract: A system, method and program product for providing self-healing for a server. A system is provided having: a server operating system (OS) and at least one application adapted to run on the server system; a system for collecting log information from the server OS and the at least one application and for forwarding the log information to a local indexing engine to generate indexed log information; a set of micro analytics engines, each adapted to analyze indexed log information associated with a respective one of the server OS and at least one application, and to generate detected anomaly conditions; and a corrective action system that inputs a detected anomaly condition against a set of micro automation codes to implement a corrective action.

Abstract: An existing data protection environment is analyzed to determine a plurality existing infrastructure containers. A popular infrastructure container is identified from the plurality of existing infrastructure containers. Responsive to determining that the popular infrastructure container does not exist within a central repository, the restore container is created within the central repository to match the popular infrastructure container.

Abstract: A solution for validating a set of data protection solutions is provided. A validation scenario can be defined, which can include data corresponding to a set of attributes for the validation scenario. The attributes can include a time frame for the validation scenario. The validation scenario also can include a set of backup images to be validated, each of which is generated using one of the set of data protection solutions. The set of backup images can be identified using the time frame. A set of resource requirements for implementing the validation scenario can be determined based on the set of backup images and the set of attributes for the validation scenario.

Abstract: A computer identifies a plurality of data retrieval requests that may be serviced using a plurality of unique data chunks. The computer services the data retrieval requests by utilizing at least one of the unique data chunks. At least one of the unique data chunks can be utilized for servicing two or more of the data retrieval requests. The computer determines a servicing sequence for the plurality of data retrieval requests such that the two or more of the data retrieval requests that can be serviced utilizing the at least one of the unique data chunks are serviced consecutively. The computer services the plurality of data retrieval requests according to the servicing sequence.

Abstract: An approach for managing and optimizing available resources utilized in a networked computing environment (e.g., a cloud computing environment) is provided. In one aspect, a plurality of resources in a plurality of storage pools within the networked computing environment are monitored. Each storage pool is running at least one application workload using a corresponding subset of the plurality of resources. The storage pools are ranked based on the application workloads. In response to an over-utilized storage pool and/or an under-utilized storage pool, the plurality of resources are dynamically reconfigured.

Abstract: Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.

Abstract: Embodiments of the present invention provide an approach for migrating virtual machines across network (e.g., WAN) separated data centers (e.g., storage clouds). Specifically, under embodiments of the present invention, a first storage system associated with a first data center is synchronized with a second storage system associated with a second data center via a storage system link. Then, a minimal state of a virtual machine is migrated from a first computer in the first data center to a second computer in the second data center via a WAN link. Using the minimal state, the virtual machine is stored in the second computer. Thereafter, the storage system link is terminated. In addition, as updated pages are received in memory of the first computer, they are migrated to the second computer via the WAN link. Once this migration is complete, the WAN link can be terminated.

Abstract: An approach for managing replicated data is presented. A current usage of resources in a system and a threshold usage of the resources are determined. Based on inter-replica correlation(s) and inter-data correlation(s) specified by a unified replication metadata model, a proper subset of replicas included in a plurality of replicas is indexed by (i) if the current usage is less than the threshold usage, determining an expected additional resource usage due to performing an indexing task online and based on the expected additional resource usage, determining a resource affinity score for performing the indexing task online, or (ii) if the current usage is greater than or equal to the threshold usage, determining an expected resource usage due to performing the indexing task offline and based on the expected resource usage, determining a resource affinity score for performing the indexing task offline.

Abstract: An approach for managing replicated data is presented. Metadata is received specifying inter-data correlation(s), inter-replica correlation(s), and data-replica correlation(s) among replicas generated for a system. A unified replication metadata model specifying the correlations is generated. Based on the inter-replica correlation(s), a proper subset of the replicas is selected. Based on the inter-replica and inter-data correlation(s), the selected proper subset of replicas is indexed to generate a unified content index. Based on a current usage of resources, an expected usage and an affinity score for performing an indexing task online or offline are determined. A query is received to locate a data item in at least one of the replicas. Based on the unified content index, the unified replication metadata model, and the query, candidate replica(s) and confidence score(s) indicating likelihood(s) that the candidate replica(s) include the data item are determined.

Abstract: Methods, systems, and articles of manufacture for networking-assisted I/O order preservation for data replication are provided herein. A method includes identifying, from one or more devices at a primary site, (i) each of multiple data changes and (ii) input/output chronological order information associated with the multiple data changes; determining one or more forwarding rules to be implemented in connection with delivering a command associated with each of said multiple data changes to one or more devices at a secondary site, wherein said forwarding rules are based on said input/output chronological order information; and delivering the command associated with each of said multiple data changes to one or more devices at the secondary site in accordance with said one or more forwarding rules to maintain a chronological order of the multiple data changes from the primary site to the secondary site.

Abstract: The present invention provides an approach to provision storage resources (e.g., across an enterprise storage system) for different workloads in an energy efficient manner. Typically, energy consumption characteristics for handling a particular storage workload will be determined. Thereafter, a type of storage device capable of handling the workload will be determined. Then, an allocation plan that results in the most efficient energy consumption for handling the workload will be developed. The allocation plan is based upon the energy consumption characteristics and an energy efficiency algorithm. The energy efficiency algorithm serves to identify storage device(s) that can handle the workload in such a way as to reduce total energy consumption and, accordingly, costs. The energy efficiency algorithm may also consider other factors such as capacity and load of storage devices and service level agreement (SLA) terms. At least one storage device can then be selected for handling the storage workload.

Abstract: A computer identifies a plurality of data retrieval requests that may be serviced using a plurality of unique data chunks. The computer services the data retrieval requests by utilizing at least one of the unique data chunks. At least one of the unique data chunks can be utilized for servicing two or more of the data retrieval requests. The computer determines a servicing sequence for the plurality of data retrieval requests such that the two or more of the data retrieval requests that can be serviced utilizing the at least one of the unique data chunks are serviced consecutively. The computer services the plurality of data retrieval requests according to the servicing sequence.

Abstract: A solution for validating a set of data protection solutions is provided. A validation scenario can be defined, which can include data corresponding to a set of attributes for the validation scenario. The attributes can include a time frame for the validation scenario. The validation scenario also can include a set of backup images to be validated, each of which is generated using one of the set of data protection solutions. The set of backup images can be identified using the time frame. A set of resource requirements for implementing the validation scenario can be determined based on the set of backup images and the set of attributes for the validation scenario.

Abstract: A solution for validating a set of data protection solutions is provided. A validation scenario can be defined, which can include data corresponding to a set of attributes for the validation scenario. The attributes can include a time frame for the validation scenario. The validation scenario also can include a set of backup images to be validated, each of which is generated using one of the set of data protection solutions. The set of backup images can be identified using the time frame. A set of resource requirements for implementing the validation scenario can be determined based on the set of backup images and the set of attributes for the validation scenario.

Abstract: A computer identifies a plurality of data retrieval requests that may be serviced using a plurality of unique data chunks. The computer services the data retrieval requests by utilizing at least one of the unique data chunks. At least one of the unique data chunks is utilized for servicing two or more of the data retrieval requests. The computer determines a servicing sequence for the plurality of data retrieval requests such that the two or more of the data retrieval requests that are serviced utilizing the at least one of the unique data chunks are serviced consecutively. The computer services the plurality of data retrieval requests according to the servicing sequence.

Abstract: In general, embodiments of present invention provide an approach for calibrating a cloud computing environment. Specifically, embodiments of the present invention provide an empirical approach for obtaining end-to-end performance characteristics for workloads in the cloud computing environment (hereinafter the “environment”). In a typical embodiment, different combinations of cloud server(s) and cloud storage unit(s) are determined. Then, a virtual machine is deployed to one or more of the servers within the cloud computing environment. The virtual machine is used to generate a desired workload on a set of servers within the environment. Thereafter, performance measurements for each of the different combinations under the desired workload will be taken. Among other things, the performance measurements indicate a connection quality between the set of servers and the set of storage units, and are used in calibrating the cloud computing environment to determine future workload placement.