Qumulo, Inc.

Noah Trent Nelson

Thomas Gregory Rothschilds

Yuxi Bai

Jonathan Michael MacLaren

Kimberly Alice Fairhurst

Thomas Scott Urban

Michael Patrick Kirby

Benjamin Gregory Reeves

Alan Francisco Delgado Duran

Austin Elery Voecks

Steven Henry Haber

Embodiments are directed to workload allocation for file system maintenance. A file system that includes storage nodes and snapshots may be provided such that each snapshot may be associated with a plurality of data blocks. If snapshots are deleted further actions may be performed, including: determining the dead blocks associated with the deleted snapshots such that each dead block may be a data block that may be unassociated with undeleted snapshots; adding the plurality of dead blocks to dead trees located on the storage nodes; determining an urgency score based on a workload model and file system metrics; determining delete tasks based on the urgency score; determining a portion of the storage nodes based on a number of delete tasks; and executing the delete tasks on the portion storage nodes to delete the dead blocks to return storage capacity to the file system.

Workload allocation for file system maintenance

Embodiments are directed to sharing namespaces across file system clusters. A file in a spoke file system may be determined based on a command provided to the spoke file system such that the file may be associated with an inode mask. Data blocks associated with the file may be determined based on the command and the inode mask such that the data blocks may be absent from the spoke file system. Requests for leases may be generated based on the absent data blocks such that each lease request corresponds to a portion of the absent data blocks Employing the leases provided by a hub file system to: copy the absent data blocks from the hub file system where each portion of absent data blocks may be associated with a lease; updating the inode mask to include the leases and each copied portion of absent blocks.

Sharing namespaces across file system clusters

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 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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Seattle, WA, United States of America

Noah Trent Nelson