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Zorro: Zero-Cost Reactive Failure Recovery in Distributed Graph Processing
Pundir, Mayank; Leslie, Luke M.; Gupta, Indranil; Campbell, Roy H.
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https://hdl.handle.net/2142/75959
Description
- Title
- Zorro: Zero-Cost Reactive Failure Recovery in Distributed Graph Processing
- Author(s)
- Pundir, Mayank
- Leslie, Luke M.
- Gupta, Indranil
- Campbell, Roy H.
- Issue Date
- 2015-05-07
- Keyword(s)
- Distributed graph processing
- Failure recovery
- Reactive approaches
- Checkpointing
- Date of Ingest
- 2015-05-07T15:51:11Z
- Abstract
- Distributed graph processing systems largely rely on proactive techniques for failure recovery. Unfortunately, these approaches (such as checkpointing) entail a significant overhead. In this paper, we argue that distributed graph processing systems should instead use a reactive approach to failure recovery. The reactive approach trades off completeness of the result (generating a slightly inaccurate result) while reducing the overhead during failure-free execution to zero. We build a system called Zorro that imbues this reactive approach, and integrate Zorro into two graph processing systems – PowerGraph and LFGraph. When a failure occurs, Zorro opportunistically exploits vertex replication (inherent in today’s graph processing systems) to quickly rebuild the state of failed servers. Experiments using real-world graphs demonstrate that Zorro is able to recover over 99% of the graph state when a few servers fail, and between 87-92% when half the cluster fails. Furthermore, using eight common graph processing algorithms, Zorro incurs little to no accuracy loss in all experimental failure scenarios.
- Type of Resource
- text
- Genre of Resource
- Technical Report
- Language
- en
- Permalink
- http://hdl.handle.net/2142/75959
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