Oracle International Corporation

Microsoft Technology Licensing, LLC

Mario Tayah

Travis Portz

Jeppe Larsen

Venkata Harish Mallipeddi

Bharath Mysore Nanjundappa

Max Verun

Jeppe Oskar Meyer Larsen

Brian L Welcker

Babak Nakhayi Ashtiani

Qian Jiang

Thomas G Patton

Sachin Shrikrishna Panvalkar

Wing Kin Thomas Chan

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 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

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.

A computer system displays a user interface display with a user input mechanism that can be actuated in order to identify a set of resources, and corresponding capacities. A team configuration is stored in memory and reflects the configuration of the resources and corresponding capacities that were identified. A task dependency structure is obtained, and is indicative of an underlying project. Resources from the stored team configuration, and corresponding capacities, are assigned to the tasks in the task dependency structure and the team configuration is updated, in memory, to reflect the assignments. A display is generated that shows the state of the underlying memory, and that is indicative of a remaining capacity and a consumed capacity.

Controlling resource allocation with automated consumption against a work breakdown structure

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Seattle, WA, United States of America

Mario Tayah