Oracle International Corporation

Wisconsin Alumni Research Foundation

Travis Portz

Mario Tayah

Jeppe Larsen

Venkata Harish Mallipeddi

Bharath Mysore Nanjundappa

Max Verun

Jeppe Oskar Meyer Larsen

Kent Lee

Qiang Wang

Deepak Agarwal

Haining Mo

Steven Gates

Li Zhang

Techniques are described that include receiving, by a computing system, a request to create a restored block volume using a first manifest, the first manifest comprising: (i) a block identifier for a block and (ii) a first block sequence number corresponding to the block identifier and associated with a first snapshot, and (iii) a manifest identifier. The techniques further include receiving, by the computing system, the request to create the restored block volume using a second manifest, the second manifest comprising: (i) the block identifier for the block, (ii) a second block sequence number corresponding to the block identifier and associated with a second snapshot. The techniques further include determining, by the computing system, whether the second block sequence number is indicative of the block having been altered after the first manifest was generated, and responsive to the determination by the computing system, creating the restored block volume.

Incremental block volume restore for optimized backup export

In accordance with an embodiment, described herein are systems and methods for automatic block storage volume tuning by simulating usage characteristics for data/block volumes. The block storage performance associated with usage by a cloud instance of a block volume can be simulated, and the manner in which data is stored or cached, for example within a combination of SSD/NVMe block storage and/or HDD object storage, can be automatically adjusted, for example to associate the block volume with a particular volume performance tier. The described approach allows the system to tune block volume performance in a dynamic manner, without further manual input from a user—the volume performance can be automatically increased when the user needs it, and otherwise reduced down to save costs (both for the user and the cloud provider). A user can enable tuning on a particular block volume, and thereafter automatically receive appropriate price/performance characteristics.

System and method for automatic block storage volume tier tuning

Techniques are provided for cross-region replication of block volume data. The techniques include a method wherein a computer system implements operations including creating a first snapshot of a block volume at a first geographic region and at a first logical time, the block volume including a plurality of partitions, transmitting first snapshot data to an object storage system at a second geographic region, creating a second snapshot of the block volume at the first geographic region and at a second logical time, generating a plurality of deltas, transmitting a plurality of delta data sets corresponding to the plurality of deltas to the object storage system at the second geographic region, generating a checkpoint at least in part by aggregating object metadata associated with the plurality of deltas and the first snapshot, receiving a restore request to generate a restore volume, and generating the restore volume from the checkpoint.

Asynchronous cross-region block volume replication

In accordance with an embodiment, described herein are systems and methods for providing tiered data storage in cloud infrastructure environments. A data storage service (block store) is adapted to automatically adjust the manner by which the data for a data volume or block volume (data/block volume), associated with a cloud instance, can be stored to meet the requirements of a performance tier. For example, responsive to selection of a particular performance tier, the storage of the data/block volume can be allocated between a first type of data storage associated with a first performance characteristics; and a second type of data storage associated with a second performance characteristics. A graphical user interface enables configuring data/block volumes to use particular performance tiers, and/or to support automatic tuning.

System and method for tiered data storage in a cloud infrastructure environment

In accordance with an embodiment, described herein are systems and methods for automatic block storage volume tuning by simulating usage characteristics for data/block volumes. The block storage performance associated with usage by a cloud instance of a block volume can be simulated, and the manner in which data is stored or cached, for example within a combination of SSD/NVMe block storage and/or HDD object storage, can be automatically adjusted, for example to associate the block volume with a particular volume performance tier. The described approach allows the system to tune block volume performance in a dynamic manner, without further manual input from a user&#x2014;the volume performance can be automatically increased when the user needs it, and otherwise reduced down to save costs (both for the user and the cloud provider). A user can enable tuning on a particular block volume, and thereafter automatically receive appropriate price/performance characteristics.

The present embodiments relate to throttling input/output (IO) processing tasks at a centralized node (e.g., a SmartNIC device) and implement multiple routing paths to prevent performance degradation in handling data patterns. For instance, the centralized node can implement a throttling system to assign portions of a number of input/output operations to be performed to a series of extent servers in a cloud infrastructure environment. The present embodiments also can implement multiple paths and/or multiple path groups to route the assigned portions of the I/O operations. For instance, the assigned portions of I/O operations can be routed to the extent servers via a first path of multiple paths. In the event of a failure of the first path, the assigned portions of I/O operations can be re-directed to a second path or another path in the path group.

Increased data processing performance of a non-volatile memory express (NVME) block store

Various embodiments are directed to generating video data using temporally offset image data samples having disparate exposure times. Synthetic samples are generated for each of the pixels at a particular time period by computing, for each synthetic sample, a combined intensity of the captured samples that fall within the time period. Synthetic samples from adjacent pixels are grouped and image data in different groups is compared to identify matching groups. Video frames are constructed by combining image data from the captured samples based upon the matched images.

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Seattle, WA, United States of America

Travis Portz