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US Patent 11995043 Map-reduce ready distributed file system

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Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119950430
Patent Inventor Names
Uppaluri Vijaya Saradhi0
Mandayam C. Srivas0
Lohit Vijaya Renu0
Amit Ashoke Hadke0
Pindikura Ravindra0
Arvind Arun Pande0
Sathya Kavacheri0
Chandra Guru Kiran Babu Sanapala0
...
Date of Patent
May 28, 2024
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Patent Application Number
183020610
Date Filed
April 18, 2023
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Patent Citations
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US Patent 7653668 Fault tolerant multi-stage data replication with relaxed coherency guarantees
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US Patent 8762493 Hierarchical spatial data structure and 3D index data versioning for generating packet data
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US Patent 8825602 Systems and methods for providing data protection in object-based storage environments
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US Patent 8850018 Consistent cluster operational data in a server cluster using a quorum of replicas
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US Patent 8898410 Efficient garbage collection in a data storage device
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US Patent 8930312 System and method for splitting a replicated data partition
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US Patent 8938425 Managing logical views of storage
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US Patent 8972345 Modifying data structures in distributed file systems
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Patent Primary Examiner
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Merilyn P Nguyen
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CPC Code
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G06F 16/128
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Patent abstract

A map-reduce compatible distributed file system that consists of successive component layers that each provide the basis on which the next layer is built provides transactional read-write-update semantics with file chunk replication and huge file-create rates. Containers provide the fundamental basis for data replication, relocation, and transactional updates. A container location database allows containers to be found among all file servers, as well as defining precedence among replicas of containers to organize transactional updates of container contents. Volumes facilitate control of data placement, creation of snapshots and mirrors, and retention of a variety of control and policy information. Also addressed is the use of distributed transactions in a map-reduce system; the use of local and distributed snapshots; replication, including techniques for reconciling the divergence of replicated data after a crash; and mirroring.

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