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An efficient and highly available framework of data recency enhancement for eventually consistent data stores

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Abstract

Data items are usually replicated in modern distributed data stores to obtain high performance and availability. However, the availability-consistency and latencyconsistency trade-offs exist in data replication, thus system designers intend to choose weak consistency models, such as eventual consistency, which may result in stale reads. Since stale data items may lead to serious application semantic problems, we consider how to increase the probability of data recency which provides a uniform view on recent versions of data items for all clients. In this work, we propose HARP, a framework that can enhance data recency of eventually consistent distributed data stores in an efficient and highly available way. Through detecting possible stale reads under failures or not, HARP can perform reread operations to eliminate stale results only when needed based on our analysis on write/read processes. We also present solutions on how to deal with some practical anomalies in HARP, including delayed, reordered and dropped messages and clock drift, and show how to extend HARP to multiple datacenters. Finally we implement HARP based on Cassandra, and the experiments show that HARP can effectively eliminate stale reads, with a low overhead (less than 6.9%) compared with original eventually consistent Cassandra.

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Acknowledgements

This work was supported partly by the National High-tech Research and Development Program (863 Program) of China (2015AA01A202), and partly by the National Natural Science Foundation of China (Grant Nos. 61370057 and 61421003).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Yu Tang, Hailong Sun, Xu Wang & Xudong Liu

Authors
  1. Yu Tang
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  2. Hailong Sun
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  3. Xu Wang
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  4. Xudong Liu
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Corresponding author

Correspondence to Xu Wang.

Additional information

Yu Tang received the BS degree from Beihang University, China in 2011. Currently, he is working towards the PhD degree in the School of Computer Science and Engineering, Beihang University. His research interests include the areas of distributed systems and availability.

Hailong Sun received the BS degree in computer science from Beijing Jiaotong University, China in 2001. He received the PhD degree in computer software and theory from Beihang University, China in 2008. He is an associate professor in the School of Computer Science and Engineering, Beihang University. His research interests include services computing, cloud computing and distributed systems. He is a member of the IEEE and the ACM.

Xu Wang received the BS degree from Beihang University, China in 2008. He received the PhD degree in computer software and theory from Beihang University in 2015. His research interests include the areas of distributed systems, service computing, replication, and availability.

Xudong Liu is a professor and dean of the School of Computer Science and Engineering, Beihang University, China. Has have leaded several China 863 key projects and e-government projects. He has published more over 30 papers, more than 10 patents. His research interests include software middleware technology, software development methods and tools, large-scale information technology projects and application of research and teaching.

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Tang, Y., Sun, H., Wang, X. et al. An efficient and highly available framework of data recency enhancement for eventually consistent data stores. Front. Comput. Sci. 11, 88–104 (2017). https://doi.org/10.1007/s11704-016-6041-1

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