Qumulo, Inc.

Yuxi Bai

Thomas Gregory Rothschilds

Jonathan Michael MacLaren

Aaron Passey

Michael Anthony Chmiel

Noah Trent Nelson

Matthew Christopher McMullan

Tyler Morrison Moody

Rowan Arthur Phipps

Kimberly Alice Fairhurst

Pathirat Kosakanchit

Steven Henry Haber

Ying Fairweather

Graham Edwin Ellis

Thorne Davis Garvin

Kevin Ross O'Neill

Tali Magidson

Philip Michael Bunge

Carson William Boden

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

Object-based storage with garbage collection and data consolidation

Embodiments are directed to tiered data store with persistent layers. A write tier in the file system for storing in a file system. A value for a performance metric that corresponds to write requests to the file system may be predicted based on characteristics of the write requests such that the performance metric may be determined based on a plurality of interactions with the write tier. The predicted value that exceeds a threshold value of the performance metric may be employed to cause performance of further actions, including: queuing a portion of the write requests in a memory buffer based on the predicted value and the threshold value; combining the queued portion of the write requests into s; storing the data segments in the write tier such that a measured value of the performance metric may be less than the threshold value.

Tiered data storage with ephemeral and persistent tiers

Embodiments are directed to in-place erasure code transcoding for distributed file systems. A file system may be divided into a first partition associated with a first erasure code and a second partition second partition is associated with a second erasure code. If the second partition has sufficient storage space to store protection groups further actions may be performed, including: determining block stores in the first partition associated with the protection groups; transcoding contents of the block stores into other block stores based on the second erasure code; storing the other block stores in the second partition; deleting the block stores from the first partition; shifting another portion of the storage space from the first partition to the second partition such that the shifted other portion increases a size of the second partition to provide sufficient storage space for other protection groups; or the like.

In-place erasure code transcoding for distributed file systems

Embodiments are directed to managing file systems over a network. A core specification that defines characteristics of a portion of a file system and parameters may be provided. The characteristics may include one or more storage device characteristics. And, the parameters may include one or more of a cluster size, or a protection level. File system models may be generated based on the core specification and the parameters. Each file system model may correspond to a file system that conforms to a core specification and specific values for the parameters. A simulation engine may be employed to provide simulation results based on the file system models. Deployment models may be generated based on the simulation results. The deployment models may be employed to provision file systems that conform to the file system performance characteristics associated with its corresponding deployment model.

Predictive performance analysis for file systems

Embodiments are directed to managing data in a file system. The file system that includes storage nodes that may be associated with storage volumes that may have a different capacity for storing data. A storage capacity of the file system may be determined based on a number of stripes of data that fit in the file system such that each stripe may be comprised of chunks that have a same chunk storage capacity. Slots in the file system that each match the chunk storage capacity may be determined based on the storage volumes. The chunks may be assigned to the slots in the file system based on the capacity of the storage nodes such that a number of chunks allocated to a same storage volume or a same storage node may be based on protection factor information.

Managing usable storage space in distributed file systems

Embodiments are directed to managing file systems over a network. Jobs may be provided to a storage computer in a file system. Control models may be associated with the jobs. Scores may be generated based on the control models. Each job may be associated with a score provided by its associated control model. And, each job that may be behind its corresponding schedule may be associated with a higher score value than each other job that may be either on its corresponding other schedule or ahead of its corresponding other schedule. Commands may be selected for execution based on the commands being associated with a job that may be associated with the higher score value that may be greater than score values associated with other jobs. The jobs may be ranked based on the updated scores. Subsequent commands may be selected and executed based on the ranking of the jobs.

Managing throughput fairness and quality of service in file systems

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Yuxi Bai