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 ecommerce application to demonstrate the benefits of this model. In large-scale systems, supporting transactions with strong consistency for serializability imposes scalability and availability limitations due to distributed coordination. Replication management protocols with weaker consistency models provide lower latencies for transactions and high availability, but guarantee only eventual consistency [6] or causal consistency [19] , [15] . Causal consistency provides more useful semantics than eventual consistency and can be supported under asynchronous replication and even under network partitions. Due to these advantages, several systems [6], [5] , [19] , [15] have been developed supporting weaker consistency models for replicated data management in large-scale systems. Our work is motivated by the observation that rather than providing a single consistency model for transactions in replicated data management systems, it is desirable to simultaneously support transactions with different levels of consistency guarantees. This allows in building scalable applications by selecting suitable transaction models for data items with different consistency requirements. Thus one can use low latency transaction management models for data items with weak consistency requirements but at the same time use transactions with strong consistency for critical data. The problem of simultaneously supporting different transaction models and consistency levels has been addressed by some recent research projects [21] , [14] , [12] . The RedBlue consistency model [14]
Tripathi and Thirunavukarasu REF presented a transaction model that simultaneously supported different consistency levels.
A transaction model for management of replicated data with multiple consistency levels
2015 IEEE International Conference on Big Data (Big Data)
2015 IEEE International Conference on Big Data (Big Data)
Computer Science