by M. Vrable et al., FAST 2012.
Abstract:We present BlueSky, a network file system backed by cloud storage. BlueSky stores data persistently in a cloud storage provider such as Amazon S3 or Windows Azure, allowing users to take advantage of the reliability and large storage capacity of cloud providers and avoid the need for dedicated server hardware. Clients access the storage through a proxy running on-site, which caches data to provide lower-latency responses and additional opportunities for optimization. We describe some of the optimizations which are necessary to achieve good performance and low cost, including a log-structured design and a secure in-cloud log cleaner. BlueSky supports multiple protocols—both NFS and CIFS—and is portable to different providers.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/bluesky.pdf
Tuesday, April 16, 2013
Tuesday, April 9, 2013
Distributed Directory Service in the Farsite File System
by J. Douceur et al., OSDI 2006.
Abstract:
We present the design, implementation, and evaluation of a fully distributed directory service for Farsite, a logically centralized file system that is physically implemented on a loosely coupled network of desktop computers. Prior to this work, the Farsite system included distributed mechanisms for file content but centralized mechanisms for file metadata. Our distributed directory service introduces tree-structured file identifiers that support dynamically partitioning metadata at arbitrary granularity, recursive path leases for scalably maintaining name-space consistency, and a protocol for consistently performing operations on files managed by separate machines. It also mitigates metadata hotspots via file-field leases and the new mechanism of disjunctive leases. We experimentally show that Farsite can dynamically partition file-system metadata while maintaining full file-system semantics.
Abstract:
We present the design, implementation, and evaluation of a fully distributed directory service for Farsite, a logically centralized file system that is physically implemented on a loosely coupled network of desktop computers. Prior to this work, the Farsite system included distributed mechanisms for file content but centralized mechanisms for file metadata. Our distributed directory service introduces tree-structured file identifiers that support dynamically partitioning metadata at arbitrary granularity, recursive path leases for scalably maintaining name-space consistency, and a protocol for consistently performing operations on files managed by separate machines. It also mitigates metadata hotspots via file-field leases and the new mechanism of disjunctive leases. We experimentally show that Farsite can dynamically partition file-system metadata while maintaining full file-system semantics.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/farsite.pdf
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/farsite.pdf
Ceph: A Scalable, High-Performance Distributed File System
by S. Weil et al., OSDI 2006.
Abstract:
We have developed Ceph, a distributed file system that provides excellent performance, reliability, and scalability. Ceph maximizes the separation between data and metadata management by replacing allocation ta- bles with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clus- ters of unreliable object storage devices (OSDs). We leverage device intelligence by distributing data replica- tion, failure detection and recovery to semi-autonomous OSDs running a specialized local object file system. A dynamic distributed metadata cluster provides extremely efficient metadata management and seamlessly adapts to a wide range of general purpose and scientific comput- ing file system workloads. Performance measurements under a variety of workloads show that Ceph has excellent I/O performance and scalable metadata manage- ment, supporting more than 250,000 metadata operations per second.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/ceph.pdf
Abstract:
We have developed Ceph, a distributed file system that provides excellent performance, reliability, and scalability. Ceph maximizes the separation between data and metadata management by replacing allocation ta- bles with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clus- ters of unreliable object storage devices (OSDs). We leverage device intelligence by distributing data replica- tion, failure detection and recovery to semi-autonomous OSDs running a specialized local object file system. A dynamic distributed metadata cluster provides extremely efficient metadata management and seamlessly adapts to a wide range of general purpose and scientific comput- ing file system workloads. Performance measurements under a variety of workloads show that Ceph has excellent I/O performance and scalable metadata manage- ment, supporting more than 250,000 metadata operations per second.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/ceph.pdf
Tuesday, April 2, 2013
The Hadoop Distributed File System
by K. Shvachko et al., MSST 2010.
Abstract:
The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers both host directly attached storage and execute user application tasks. By distributing storage and computation across many servers, the resource can grow with demand while remaining economical at every size. We describe the architecture of HDFS and report on experience using HDFS to manage 25 petabytes of enterprise data at Yahoo!.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/hfs.pdf
Abstract:
The Hadoop Distributed File System (HDFS) is designed to store very large data sets reliably, and to stream those data sets at high bandwidth to user applications. In a large cluster, thousands of servers both host directly attached storage and execute user application tasks. By distributing storage and computation across many servers, the resource can grow with demand while remaining economical at every size. We describe the architecture of HDFS and report on experience using HDFS to manage 25 petabytes of enterprise data at Yahoo!.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/hfs.pdf
The Google File System
by S. Ghemewat., SOSP 2003.
Abstract:
We have designed and implemented the Google File Sys- tem, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients.
While sharing many of the same goals as previous dis- tributed file systems, our design has been driven by obser- vations of our application workloads and technological envi- ronment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us to reexamine traditional choices and explore rad- ically different design points.
The file system has successfully met our storage needs. It is widely deployed within Google as the storage platform for the generation and processing of data used by our ser- vice as well as research and development efforts that require large data sets. The largest cluster to date provides hun- dreds of terabytes of storage across thousands of disks on over a thousand machines, and it is concurrently accessed by hundreds of clients.
In this paper, we present file system interface extensions designed to support distributed applications, discuss many aspects of our design, and report measurements from both micro-benchmarks and real world use.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/gfs.pdf
We have designed and implemented the Google File Sys- tem, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients.
While sharing many of the same goals as previous dis- tributed file systems, our design has been driven by obser- vations of our application workloads and technological envi- ronment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us to reexamine traditional choices and explore rad- ically different design points.
The file system has successfully met our storage needs. It is widely deployed within Google as the storage platform for the generation and processing of data used by our ser- vice as well as research and development efforts that require large data sets. The largest cluster to date provides hun- dreds of terabytes of storage across thousands of disks on over a thousand machines, and it is concurrently accessed by hundreds of clients.
In this paper, we present file system interface extensions designed to support distributed applications, discuss many aspects of our design, and report measurements from both micro-benchmarks and real world use.
Link to the full paper:
http://www.cse.buffalo.edu/faculty/tkosar/cse710_spring13/papers/gfs.pdf
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