Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for recovering data across relational and non-relational databases. In a particular embodiment, a method includes identifying first data having a first version time in a first version store storing data versions from a relational database. Based on the first version time, the method provides identifying second data, associated with the first data, in a second version store storing data versions from a non-relational database. The method further provides recovering the first data to the relational database and the second data to the non-relational database.

Abstract: A method of restoring version data stored across two or more cloud environments is provided. An example method includes accessing, in a second cloud environment, first metadata describing a first data version, the first data version including first data items and first metadata, wherein at least the first data items are stored in a first cloud environment and the first metadata is stored in a third cloud environment. In response to an instruction received in the second cloud environment, the first data items are restored to the second cloud environment using the first metadata.

Abstract: The technology disclosed herein enables the restoration of a database version across cloud environments. In a particular embodiment, a method provides receiving, in a second cloud environment from a first cloud environment, first metadata describing a first data version stored in the first cloud environment. The first data version includes first data items and the first metadata. After receiving the first metadata, the method provides receiving, in the second cloud environment, an instruction to restore the first data items to the second cloud environment. In response to the instruction, the method provides restoring the first data items to the second cloud environment using the first metadata.

Abstract: A method of restoring a database across cloud environments is provided. An example method includes receiving, in a second cloud environment from a first cloud environment, first metadata describing a first data version stored in the first cloud environment. The first metadata describes where first data items are located within the first data version stored in the first cloud environment, includes an access procedure or protocol such that the second cloud environment can access the first data items, and includes authorization information necessary to confirm that the second cloud environment is authorized to access the first data items. In response to an instruction, the first data items are restored to the second cloud environment using the first metadata.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for infinite versioning by automatic coalescing. In a particular embodiment, a method provides determining an age range for a plurality of data versions stored in a secondary data repository and identifying first data versions of the plurality of data versions that are within the age range. The method further provides determining a compaction ratio for the first data versions and compacting the first data versions based on the compaction ratio.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for infinite versioning by automatic coalescing. In a particular embodiment, a method provides determining an age range for a plurality of data versions stored in a secondary data repository and identifying first data versions of the plurality of data versions that are within the age range. The method further provides determining a compaction ratio for the first data versions and compacting the first data versions based on the compaction ratio.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for infinite versioning by automatic coalescing. In a particular embodiment, a method provides determining an age range for a plurality of data versions stored in a secondary data repository and identifying first data versions of the plurality of data versions that are within the age range. The method further provides determining a compaction ratio for the first data versions and compacting the first data versions based on the compaction ratio.

Abstract: Described herein is a system that restores a database by processing a portion of the database. The system restores the database to a previous state at a particular time by reverting data entries that have changed since the time to their initial values before the change. Data entries that have changed after the restore time are identified. For the data entries that have changed after the restore time, their initial values before the change are determined from various sources. The system determines a database version that is created most recently before the restore time. The system additionally identifies changes to the database between the restore time and when the database version is created. The initial values can be determined from either the database version or the changes made to the database between the restore time and when the database version is created.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media to enhance the organization, storage, and retrieval of data. In a particular embodiment, a method provides storing a plurality of incremental data versions in a data repository and maintaining first time information for data items in each of the incremental data versions. The method further provides identifying a first data version of the plurality of incremental data versions and writing data in the data repository to create a second data version based the first data version. The method then provides second time information associated with the creation of the second data and creation of the first data version.

Abstract: A method and system for optimizing power consumption of a data center by dynamic workload adjustment. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for sub-cluster recovery in a data storage environment having a plurality of storage nodes. In a particular embodiment, the method provides scanning data items in the plurality of nodes. While scanning, the method further provides indexing the data items into an index of a plurality of partition groups. Each partition group includes data items owned by a particular one of the plurality of storage nodes. The method then provides storing the index.

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: Embodiments disclosed herein provide systems, methods, and computer readable media for recovering data across relational and non-relational databases. In a particular embodiment, a method includes identifying first data having a first version time in a first version store storing data versions from a relational database. Based on the first version time, the method provides identifying second data, associated with the first data, in a second version store storing data versions from a non-relational database. The method further provides recovering the first data to the relational database and the second data to the non-relational database.

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: Embodiments of the invention relate to receiving a write request that includes a write data and an address of a target block in tertiary storage. In response to the write request, a write-miss is detected at a cache located in persistent storage. Based on detecting the write-miss, the write data and associated metadata are written to a fast write storage location and the write request is marked as complete. In addition, the target block is retrieved from the address in the tertiary storage and stored in the cache. Contents of the fast write storage location are merged with the contents of the target block in the cache.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for sub-cluster recovery in a data storage environment having a plurality of storage nodes. In a particular embodiment, the method provides scanning data items in the plurality of nodes. While scanning, the method further provides indexing the data items into an index of a plurality of partition groups. Each partition group includes data items owned by a particular one of the plurality of storage nodes. The method then provides storing the index.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for searching content in versioned database data. In a particular embodiment, a method provides obtaining a first data version of database data and indexing the first data version to create a first index. The first index includes a time indicator corresponding to creation of the first data version. The method further provides incorporating the first index into a searchable index of one or more additional data versions. The searchable index includes one or more time indicators that each correspond to a respective one of the one or more additional data versions. Additionally, the method provides receiving a search query including at least one of an event, time, and/or time range parameter and returning information from the searchable index that satisfies the time parameter.

Abstract: Embodiments disclosed herein provide systems, methods, and computer readable media for generating a consistent deduplicated snapshot of a distributed database using optimistic deduplication. In a particular embodiment, a method provides, for each node of a plurality of nodes in the distributed database, deduplicating data items stored on the node that are owned by the node and generating a summary that describes a file in which the data items are located. The method further provides identifying from the summaries for each of the nodes whether mistakes occurred during deduplication and, upon identifying one or more mistakes, determining one or more corrections for the one or more mistakes. Also, the method provides generating a consistent deduplicated snapshot for the distributed database comprising the deduplicated data items from each node and the one or more corrections.

Abstract: A method and system for optimizing power consumption of a data center by dynamic workload adjustment. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.

Abstract: A method and system for optimizing power consumption of a data center by dynamic workload adjustment. At least one candidate workload solution for the data center is generated. Each candidate workload solution represents a respective application map that specifies a respective workload distribution among application programs of the data center. Workload of the data center is dynamically adjusted from a current workload distribution to an optimal workload solution. The optimal workload solution is a candidate workload solution of the at least one candidate workload solution having a lowest sum of a respective power cost and a respective migration cost. Dynamically adjusting the workload of the data center includes: estimating a respective overall cost of each candidate workload solution, selecting the optimal workload solution that has a lowest overall cost as determined from the estimating, and transferring the optimal workload solution to devices of a computer system for deployment.