Application Level Fault Tolerance in Heterogeneous Networks of Workstations

Beguelin, Adam; Seligman, Erik; Stephan, Peter

doi:10.1006/jpdc.1997.1338

Cited by 88 publications

(30 citation statements)

References 17 publications

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“…Semi-transparent approaches [8,42] provide APIs to specify when and what to checkpoint. Transparent approaches [8,28] use preprocessors to produce checkpointcapable code. SGuard is more transparent than semitransparent approaches and more fine-gained than static or runtime analysis ones.…”

Section: Related Workmentioning

confidence: 99%

“…SGuard is more transparent than semitransparent approaches and more fine-gained than static or runtime analysis ones. Additionally, unlike SGuard, some of the above techniques do not provide concurrent checkpointing [8] and many require expensive state serialization [8]. The C 3 [10] application-level checkpointing system is most similar to SGuard in that it also uses a custom memory manager.…”

Section: Related Workmentioning

confidence: 99%

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Fault-tolerant stream processing using a distributed, replicated file system

2008

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We present SGuard, a new fault-tolerance technique for distributed stream processing engines (SPEs) running in clusters of commodity servers. SGuard is less disruptive to normal stream processing and leaves more resources available for normal stream processing than previous proposals. Like several previous schemes, SGuard is based on rollback recovery [18]: it checkpoints the state of stream processing nodes periodically and restarts failed nodes from their most recent checkpoints. In contrast to previous proposals, however, SGuard performs checkpoints asynchronously: i.e., operators continue processing streams during the checkpoint thus reducing the potential disruption due to the checkpointing activity. Additionally, SGuard saves the checkpointed state into a new type of distributed and replicated file system (DFS) such as GFS [22] or HDFS [9], leaving more memory resources available for normal stream processing. To manage resource contention due to simultaneous checkpoints by different SPE nodes, SGuard adds a scheduler to the DFS. This scheduler coordinates large batches of write requests in a manner that reduces individual checkpoint times while maintaining good overall resource utilization. We demonstrate the effectiveness of the approach through measurements of a prototype implementation in the Borealis [2] open-source SPE using HDFS [9] as the DFS.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fault-tolerant stream processing using a distributed, replicated file system

2008

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“…Well-known examples are the Isis [6] and Horus [54] systems at Cornell University, the Totem system at the University of California, Santa Barbara [1], [40], and the Transis system at the Hebrew University of Jerusalem [19], [20]. Projects focusing on fault tolerance through process replication and rollback-recovery include the Manetho project at Rice University [25] and the DOME project at Carnegie Mellon University [4], [18]. RAID techniques are widely used for performance and reliability in storage systems [17].…”

Section: Related Workmentioning

confidence: 99%

Computing in the RAIN: A Reliable Array of Independent Nodes

Bohossian

Fan

LeMahieu

et al. 2000

Lecture Notes in Computer Science

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AbstractÐThe RAIN project is a research collaboration between Caltech and NASA-JPL on distributed computing and data storage systems for future spaceborne missions. The goal of the project is to identify and develop key building blocks for reliable distributed systems built with inexpensive off-the-shelf components. The RAIN platform consists of a heterogeneous cluster of computing and/or storage nodes connected via multiple interfaces to networks configured in fault-tolerant topologies. The RAIN software components run in conjunction with operating system services and standard network protocols. Through software-implemented fault tolerance, the system tolerates multiple node, link, and switch failures, with no single point of failure. The RAIN technology has been transfered to Rainfinity, a start-up company focusing on creating clustered solutions for improving the performance and availability of Internet data centers. In this paper, we describe the following contributions: 1) fault-tolerant interconnect topologies and communication protocols providing consistent error reporting of link failures, 2) fault management techniques based on group membership, and 3) data storage schemes based on computationally efficient error-control codes. We present several proof-of-concept applications: a highly-available video server, a highly-available Web server, and a distributed checkpointing system. Also, we describe a commercial product, Rainwall, built with the RAIN technology.

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“…In particular, checkpointing strategies may take advantage of the replication and redundancy inherent in shared memory systems to achieve better performance. This has been explored by researchers for transparent runtime libraries that implement distributed shared memory [21][22][23][24], and for programs that make explicit use of data structures with shared memory semantics [25,26].…”

Section: Distributed Shared Memory and Other Programming Paradigmsmentioning

confidence: 99%

Deploying fault tolerance and taks migration with NetSolve

Plank

Casanova

Beck

et al. 1999

Future Generation Computer Systems

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Computational power grids are computing environments with massive resources for processing and storage. While these resources may be pervasive, harnessing them is a major challenge for the average user. NetSolve is a software environment that addresses this concern. A fundamental feature of NetSolve is its integration of fault-tolerance and task migration in a way that is transparent to the end user. In this paper, we discuss how NetSolve's structure allows for the seamless integration of fault-tolerance and migration in grid applications, and present the specific approaches that have been and are currently being implemented within NetSolve.

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Application Level Fault Tolerance in Heterogeneous Networks of Workstations

Cited by 88 publications

References 17 publications

Fault-tolerant stream processing using a distributed, replicated file system

Fault-tolerant stream processing using a distributed, replicated file system

Computing in the RAIN: A Reliable Array of Independent Nodes

Deploying fault tolerance and taks migration with NetSolve

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