Atlantis Computing, Inc.

Cisco Technology, Inc.

Netapp, Inc.

Sagar Shyam Dixit

Chetan Venkatesh

Shravan Gaonkar

Kartikeya Iyer

Vinodh Dorairajan

Systems and methods provide for optimizing data deduplication. A physical storage node can determine a cost of performing each exclusive action of a set of exclusive actions associated with data deduplication for a predetermined number of epochs to populate an exclusive action cost matrix. The node can select an optimal exclusive action from among the set of exclusive actions based on the cost of performing each exclusive action. In response to a random number being below a threshold value, the node can determine a first cost of performing a randomly selected exclusive action from the set of exclusive actions and update the exclusive action cost matrix with the first cost. In response to the random number exceeding the threshold value, the node can determine a second cost of performing the optimal exclusive action and update the exclusive action cost matrix with the second cost.

Reinforcement learning for optimizing data deduplication

Techniques for fast provisioning of virtual machine images using deduplication metadata are described, including receiving a request to copy a first virtual machine to form a second virtual machine, identifying a first portion of memory comprising data for the first virtual machine; and forming the second virtual machine based on the first portion of memory comprising data for the first virtual machine, wherein forming the second virtual machine further comprises linking the second virtual machine to the first portion of memory comprising data for the first virtual machine, and implementing a second portion of memory to store data for the second virtual machine independent of the first virtual machine.

Efficient provisioning of cloned virtual machine images using deduplication metadata

Techniques for deduplication of virtual machine files in a virtualized desktop environment are described, including receiving data into a page cache, the data being received from a virtual machine and indicating a write operation, and deduplicating the data in the page cache prior to committing the data to storage, the data being deduplicated in-band and in substantially real-time.

Deduplication of virtual machine files in a virtualized desktop environment

Methods and systems to intelligently cache content in a virtualization environment using virtualization software such as VMWare ESX or Citrix XenServer or Microsoft HyperV or Redhat KVM or their variants are disclosed. Storage IO operations (reads from and writes to disk) are analyzed (or characterized) for their overall value and pinned to cache if their value exceeds a certain defined threshold based on criteria specific to the New Technology File System (NTFS) file-system. Analysis/characterization of NTFS file systems for intelligent dynamic caching include analyzing storage block data associated with a Virtual Machine of interest in accordance with a pre-determined data model to determine the value of the block under analysis for long term or short term caching. Integer values assigned to different types of NTFS objects in a white list data structure called a catalog that can be used to analyze the storage block data.

Method and apparatus for preparing a cache replacement catalog

Systems and methods for intelligent content aware caching

Method and apparatus for servicing read and write requests using a cache replacement catalog

Method and apparatus for cache replacement using a catalog

Methods and systems for efficiently determining a similarity between two or more datasets. In one embodiment, the similarity is determined based on comparing a subset of sorted frequency-weighted blocks from one dataset to a subset of sorted frequency-weighed blocks from another dataset. Data blocks of a dataset are converted into hash values that are frequency-weighted. These frequency-weighted hash values can be compared to frequency-weighted hash values of another dataset to determine a similarity of the two datasets. In another embodiment, upon a change of a block in a subset of the dataset, the similarity value is re-determined without resorting or hashing the blocks of a dataset other than the blocks of the subset, resulting in an increased performance of a similarity comparison. In another embodiment, blocks of a dataset are excluded based on a block-filtering rule to increase the accuracy of the similarity comparison.

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Sunnyvale, CA, United States of America

Sagar Shyam Dixit