Tintri by Ddn, Inc.

Tintri Inc.

Trintri Inc.

Mark G Gritter

Edward Kihyen Lee

Pratap Vikram Singh

Raj Ashok Sudarsanam

Siva Popuri

Shobhit Dayal

Vyacheslav Vladimirovich Malyugin

Satya Vempati

Kieran J Harty

Umesh Patil

Luong Duong

Karthik Ravichandra

Rex R Walters

Dattatraya Koujalagi

Rose F Liu

Soujanya Shankaranarayana

Kavita Agarwal

Nikhil Pujari

Zubin D Dittia

Brandon W Salmon

Hong Wang

William Lim

Load balancing VM selection and movement is disclosed, including: selecting a combined predicted metric to be improved with respect to a plurality of storage devices; and determining a set of VMs to be moved from a first storage device included in the plurality of storage devices to a second storage device included in the plurality of storage devices such that the selected combined metric to improve is predicted to improve.

Load balancing VM selection and movement

Offloading techniques to assist a hypervisor with VM data migration between storage devices are disclosed. In an embodiment, a method to offload live data migration from hypervisors includes migrating snapshots from a source device to a destination device, where the snapshots are associated with a virtual machine (VM) being migrated from a source device to a destination device. The method includes requesting a hypervisor to take a hypervisor snapshot. The method includes taking a storage array-based snapshot after requesting the hypervisor to take the hypervisor snapshot, and requesting the hypervisor to migrate live data from the source device to the destination device including by using the hypervisor snapshot to offload hypervisor live data migration. Hypervisor offload can be performed for various hypervisors and protocols including vSphere Storage APIs Array Integration (VAAI) and Remote offloaded data transfer (ODX).

Hypervisor remote offload for VM data migration between storage devices

VM data migration between storage devices is disclosed. In an embodiment, a method of VM data migration includes determining snapshot data and live data corresponding to the VM based on a request to migrate a VM from a source device to a destination device. The method migrates the snapshot data from the source device to the destination device including by setting up a migration path and creating a migration configuration in the source device. The method requests a hypervisor to migrate the live data from the source device to the destination device. The method removes the migration path, the migration configuration, and snapshot data from the source device.

VM data migration between storage devices

Modeling space consumption of a migrated VM is disclosed, including: obtaining aggregated effective historical space usage data associated with a plurality of VMs at a storage device; using the aggregated effective historical space usage data to generate one or more predicted metrics of space usage at the storage device; and using the one or more predicted metrics of space usage to potentially migrate a VM with respect to the storage device.

Modeling space consumption of a migrated VM

Performing data synchronization is disclosed, including: receiving an indication to synchronize a container to a snapshot, wherein the container has a first data state and an identity, wherein the snapshot corresponds to a second data state; causing the container to have the second data state corresponding to the snapshot; and maintaining the identity of the container.

Efficient data synchronization for storage containers

Storing data is disclosed, including: creating a clone of a snapshot comprising a first set of metadata associated with a source data, at least in part by generating for the clone a second set of metadata that includes an active index that is associated with the snapshot and is configured to store metadata associated with data values that are written to the clone; receiving a write operation to write a requested data value to the clone; and in response to the write operation, updating the active index based at least in part on the requested data value.

Efficient and flexible organization and management of file metadata

Aggregating data associated with cluster virtual machines is disclosed, including obtaining from a plurality of storage systems a plurality of values associated with an attribute associated with a cluster virtual machine (VM); and aggregating the plurality of values associated with the attribute based at least in part on an aggregation semantic associated with the attribute.

Cluster virtual machines

Load balancing using predictive VM-based analytics is disclosed, including: determining a plurality of storage device specific predicted metric data structures corresponding to respective ones of a plurality of storage devices; and combining the plurality of storage device specific predicted metric data structures corresponding to respective ones of the plurality of storage devices into a combined predicted metric data structure.

Load balancing using predictive VM-based analytics

Access rate prediction in a hybrid storage device is disclosed, including: using a first tier type of storage access counts to model an access distribution across a first tier type of storage and a second tier type of storage at a storage device; and generating a predicted first tier type of storage hit rate that is predicted to result from moving an existing VM off of the storage device using access statistical information associated with the existing VM and the access distribution at the storage device.

Access rate prediction in a hybrid storage device

Detecting and pinpointing data corruption is disclosed, including: storing an object-level metadata structure corresponding to a stored object, wherein the stored object comprises a plurality of blocks; and determining for a block included in the plurality of blocks, based at least in part on a piece of identifying information of the block, two or more locations in the object-level metadata structure at which to store a value computed based at least in part on data comprising the block.

Detecting and pinpointing data corruption

Avoiding long access latencies in redundant storage systems is disclosed, including: determining a first device associated with a request is in a slow access period based at least in part on a scheduled slow access period, wherein the first device is included in a plurality of devices associated with a redundant storage system; and reconstructing data associated with the request from one or more devices included in the plurality of devices other than the first device.

Avoiding long access latencies in redundant storage systems

Synchronizing data is disclosed, including: generating a snapshot index associated with a first set of data; generating a set of difference data between a snapshot index associated with a second set of data and the snapshot index associated with the first set of data; and applying the set of difference data to a set of metadata associated with the first set of data, wherein the set of metadata reflects a current state of the first set of data.

Growing community of inventors

Eagan, MN, United States of America

Mark G Gritter