Rubrik, Inc.

Garvit Juniwal

Adam Gee

Arvind Nithrakashyap

Gaurav Jain

Arijit Banerjee

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 identifying a set of disks within a cluster and then storing a plurality of data chunks into the set of disks such that the placement of the plurality of data chunks within the cluster optimizes failure tolerance and storage system performance for the cluster are described. The plurality of data chunks may be generated using replication of data (e.g., n-way mirroring) or application of erasure coding to the data (e.g., using a Reed-Solomon code or a Low-Density Parity-Check code). The topology of the cluster including the physical arrangement of the nodes and disks within the cluster and status information for the nodes and disks within the cluster (e.g., information regarding disk fullness, disk performance, and disk age) may be used to identify the set of disks in which to store the plurality of data chunks.

Chunk allocation

Methods and systems for improving the read and write performance of a distributed file system while limiting memory usage are described. The type of error correcting scheme applied to data, the partitioning of the data into data chunks, and the sizes of data slices within each of the data chunks used for storing electronic files within the distributed file system may be dynamically adjusted over time to optimize for fast IO performance while limiting memory usage (e.g., requiring less than 256 MB of RAM to generate and store code blocks). The file size of an electronic file to be stored, the amount of available memory for generating code blocks, and the amount of available disk space to store the electronic file may be used to set the data sizes of the data slices and the type of erasure code applied to data blocks associated with the data slices.

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Mountain View, CA, United States of America

Garvit Juniwal