A Transaction Model for Management of Replicated Data with Multiple Consistency Levels
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A Transaction Model for Management of Replicated Data with Multiple Consistency Levels
Published Date
2016
Publisher
IEEE
Type
Conference Paper
Abstract
We present a transaction model which simultaneously supports different consistency levels, which include serializable transactions for strong consistency, and weaker consistency models such as causal snapshot isolation (CSI), CSI with commutative updates, and CSI with asynchronous updates. This model
is useful in managing large-scale replicated data with different consistency guarantees to make suitable trade-offs between data consistency and performance. Data and the associated transactions are organized in a hierarchy which is based on consistency levels. Certain rules are imposed on transactions to constrain information flow across data at different levels in this hierarchy to ensure the required consistency guarantees. The building block
for this transaction model is the snapshot isolation model. We present an example of an e-commerce application structured with data items and transactions defined at different consistency levels. We have implemented a testbed system for replicated data management based on the proposed multilevel consistency model. We present here the results of our experiments with this e-commerce application to demonstrate the benefits of this model.
Description
This paper presents a novel scheme for distributed data replication management systems to support different data consistency models.
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National Science Foundation
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10.1109/BigData.2015.7363788
Previously Published Citation
A. Tripathi and B. Thirinavukarasu, "A Transaction Model for Management of Replicated Data with Multiple Consistency Levels," 2015 IEEE International Conference on Big Data (Big Data), DOI: 10.1109/BigData.2015.7363788
Suggested citation
Tripathi, Anand; Thirunavukarasu, BhagavathiDhass. (2016). A Transaction Model for Management of Replicated Data with Multiple Consistency Levels. Retrieved from the University Digital Conservancy, 10.1109/BigData.2015.7363788.
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