Google Inc.

Rubrik, Inc.

Archit Gupta

Harish Chandran

Gaurav Maheshwari

Hariharan Chandrasekaran

Shivanshu Agrawal

Srikanth Hanumanula

Anuj Mittal

Kritagya Dabi

Arpit Kathuria

Julian Rajeshwar

Shruti Jain

Asim Fazal

Tanuj Sharma

Srivatsan Narayanan

Kashish Bansal

Chris Terefinko

Nishir Shelat

Luiz Franca Pereira Filho

Tim Sears

A data management system (DMS) may facilitate the storage tiering of snapshots on cloud environments. For example, the DMS may transmit snapshot signaling to a first cloud environment that instructs the first cloud environment to capture a first snapshot of a computing object and to store the first snapshot in a first type of cloud storage at the first cloud environment. The DMS may determine that the first snapshot has been stored in the first type of cloud storage for a duration of time that satisfies an archival threshold. Based on the archival threshold being satisfied, the DMS may transmit archival signaling that instructs the first cloud environment to store the first snapshot to a second type of cloud storage. The second type of cloud storage may be associated with a longer access latency than the first type of cloud storage.

Storage tiering for computing system snapshots

Training and/or utilizing a machine learning model to generate request agnostic predicted interaction scores for electronic communications, and to utilization of request agnostic predicted interaction scores in determining whether, and/or how, to provide corresponding electronic communications to a client device in response to a request. A request agnostic predicted interaction score for an electronic communication provides an indication of quality of the communication, and is generated independent of corresponding request(s) for which it is utilized. In many implementations, a request agnostic predicted interaction score for an electronic communication is generated 'offline' relative to corresponding request(s) for which it is utilized, and is pre-indexed with (or otherwise assigned to) the electronic communication. This enables fast and efficient retrieval, and utilization, of the request agnostic interaction score by computing device(s), when the electronic communication is responsive to a request.

Generating, using a machine learning model, request agnostic interaction scores for electronic communications, and utilization of same

Techniques are described herein for using operational transforms to perform operations on parallel copies of a document model. A method includes: determining that a first operation is to be performed on a second parallel copy; and in response: determining that a revision of a first parallel copy matches a revision of the second parallel copy; and in response: performing the first operation on the second parallel copy to obtain a calculation result including a first list of commands; applying the first list of commands to the second parallel copy; performing an operational transform on at least one command in the first list of commands based on queued user edits to the first parallel copy, the queued user edits including a second list of commands, to obtain a transformed list of commands; and applying the transformed list of commands to the first parallel copy.

Using operational transforms to perform operations on parallel copies of a document model

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Mountain View, CA, United States of America

Archit Gupta