Rubrik, Inc.

Oracle International Corporation

Arvind Nithrakashyap

Soham Mazumdar

Arvind Jain

Jonathan Carlyle Derryberry

Kenny To

Juan R Loaiza

Fabiano C Botelho

Tirthankar Lahiri

Bipul Sinha

Adam Gee

Garvit Juniwal

Roger J Bamford

Vikram Joshi

Alok Pareek

Jayanth Madheswaran

Amit Ganesh

Jonathan D Klein

Gary C Ngai

William Bridge

Vinay H Srihari

J William Lee

Srinivas Vemuri

Ari Wolfe Mozes

Wei Ming Hu

Kant C Patel

Jiangbin Luo

Kiran Badrinarain Goyal

Sreedhar Mukkamalla

Chi Young Ku

Sharvanath Pathak

Poojan Kumar

Aravind Menon

Tudor Bosman

Brian Hirano

Techniques for preventing recovery of specific data elements based on a recovery prevention configuration defined by a user are disclosed. In some embodiments, a computer system performs operations comprising: receiving a recovery prevention configuration from a first computing device of a first user, the recovery prevention configuration comprising at least one recovery prevention parameter specified by the first user via one or more user interface elements displayed on the first computing device, the at least one recovery prevention parameter being configured to identify one or more backup data elements stored in a secondary storage system; storing the recovery prevention configuration in a database in association with the secondary storage system; and filtering out the one or more backup data elements stored in the secondary storage system from a data recovery process based on the at least one recovery prevention parameter of the stored recovery prevention configuration.

Preventing recovery of specific data elements

Methods and systems for improving the performance of a primary system that is running one or more virtual machines and capturing snapshots of the one or more virtual machines over time are described. The performance penalty on the primary system when a hypervisor running the one or more virtual machines is used to capture the snapshots of the one or more virtual machines may be reduced by leveraging storage array snapshots to reduce the amount of time that the hypervisor must freeze virtual disks of the one or more virtual machines. In this case, changed block tracking information for changed data blocks associated with the snapshots may be acquired from the hypervisor and the changed data blocks themselves may be pulled from the storage array snapshots without requiring the hypervisor to keep the virtual disks of the one or more virtual machines in a frozen state.

Array integration for virtual machine backup

Methods and systems for managing, storing, and serving data within a virtualized environment are described. In some embodiments, a data management system may manage the extraction and storage of virtual machine snapshots, provide near instantaneous restoration of a virtual machine or one or more files located on the virtual machine, and enable secondary workloads to directly use the data management system as a primary storage target to read or modify past versions of data. The data management system may allow a virtual machine snapshot of a virtual machine stored within the system to be directly mounted to enable substantially instantaneous virtual machine recovery of the virtual machine.

Data management system

Network optimized deduplication of virtual machine snapshots

Converged search and archival system

Automated generation of cloned production environments

Management of virtual machine snapshots

Identification of virtual machines using a distributed job scheduler

Allocation of memory is optimized across multiple pools of memory, based on minimizing the time it takes to successfully retrieve a given data item from each of the multiple pools. First data is generated that indicates a hit rate per pool size for each of multiple memory pools. In an embodiment, the generating step includes continuously monitoring attempts to access, or retrieve a data item from, each of the memory pools. The first data is converted to second data that accounts for a cost of a miss with respect to each of the memory pools. In an embodiment, the second data accounts for the cost of a miss in terms of time. How much of the memory to allocate to each of the memory pools is determined, based on the second data. In an embodiment, the steps of converting and determining are automatically performed, on a periodic basis.

Techniques for automated allocation of memory among a plurality of pools

A number, of the blocks of data to be prefetched into a buffer cache, is determined dynamically at run time (e.g. during execution of a query), based at least in part on the load placed on the buffer cache. An application program (such as a database) is responsive to the number (also called 'prefetch size'), to determine the amount of prefetching. A sequence of instructions (also called 'prefetch size daemon') computes the prefetch size based on, for example, the number of prefetched blocks aged out before use. The prefetch size daemon dynamically revises the prefetch size based on usage of the buffer cache, thereby to form a feedback loop. Depending on the embodiment, at times of excessive use of the buffer cache, prefetching may even be turned off. Although in one embodiment described herein the prefetch size daemon is implemented in a database, in other embodiments other kinds of applications and/or the operating system itself can use a prefetch size daemon of the type described herein to dynamically determine and change prefetch behavior.

System load based adaptive prefetch

Growing community of inventors

San Francisco, CA, United States of America

Arvind Nithrakashyap