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2017 46th International Conference on Parallel Processing (ICPP) (2017)
Bristol, United Kingdom
Aug. 14, 2017 to Aug. 17, 2017
ISSN: 2332-5690
ISBN: 978-1-5386-1042-8
pp: 571-580
Apache Storm is a fault-tolerant, distributed inmemory computation system for processing large volumes of high-velocity data in real-time. As an integral part of the fault-tolerance mechanism, Storm's state management is achieved by a checkpointing framework, which commits states regularly and recovers lost states from the latest checkpoint. However, this method involves a remote data store for state preservation and access, resulting in significant overheads to the performance of error-free execution.In this paper, we propose E-Storm, a replication-based state management system that actively maintains multiple state backups on different worker nodes. We build a prototype on top of Storm by extending it with monitoring and recovery modules to support inter-task state transfer whenever needed. The experiments carried out on synthetic and real-world streaming applications confirm that E-Storm outperforms the existing checkpointing method in terms of the resulting application performance, obtaining as much as 9.44 times throughput improvement while reducing the application latency down to 9.8%.
Storms, Monitoring, Topology, Distributed databases, Fault tolerance, Fault tolerant systems, Checkpointing

X. Liu, A. Harwood, S. Karunasekera, B. Rubinstein and R. Buyya, "E-Storm: Replication-Based State Management in Distributed Stream Processing Systems," 2017 46th International Conference on Parallel Processing (ICPP), Bristol, United Kingdom, 2017, pp. 571-580.
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