Causally Coordinated Snapshot Isolation for Geographically Replicated Data

Padhye, Vinit; Tripathi, Anand

doi:10.1109/srds.2012.15

Cited by 7 publications

(9 citation statements)

References 9 publications

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“…This is called the fork-join model. The CSI [17] model improves upon the PSI model to reduce false causal dependencies.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper we present a transaction management model for managing replicated data with different consistency levels to make suitable trade-offs between data consistency and performance. This transaction management model is based on snapshot isolation (SI) [2] with weaker semantics that guarantee causal consistency of snapshots as proposed in [19], [17]. We use the CSI [17] model as a building-block in our work.…”

Section: Introductionmentioning

confidence: 99%

“…This transaction management model is based on snapshot isolation (SI) [2] with weaker semantics that guarantee causal consistency of snapshots as proposed in [19], [17]. We use the CSI [17] model as a building-block in our work. The proposed model simultaneously supports transactions with different consistency models, which include serializability for strong consistency, and weaker models such as CSI (causal snapshot isolation), CSI with commutative updates, and CSI with asynchronous concurrent updates.…”

Section: Introductionmentioning

confidence: 99%

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A transaction model for management of replicated data with multiple consistency levels

Tripathi

Thirunavukarasu

2015

2015 IEEE International Conference on Big Data (Big Data)

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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.

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“…This is called the fork-join model. The CSI [17] model improves upon the PSI model to reduce false causal dependencies.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A transaction model for management of replicated data with multiple consistency levels

Tripathi

Thirunavukarasu

2015

2015 IEEE International Conference on Big Data (Big Data)

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“…In contrast, PCSI uses vector clocks effectively to avoid maintaining any dependency information on per object level, and it does not require that updates on a partition be serialized. Only the updates on a given object are serialized which is a result of the no write-write conflict requirement, PCSI also minimizes causal dependencies by considering only the read-write sets of a transaction [26]. For obtaining the start snapshot, NMSI supports the notion of forward freshness, whereas in PCSI it is obtained in the beginning and it is "frozen".…”

Section: Related Workmentioning

confidence: 99%

“…The start snapshot may contain events, or in other words item versions, that are not read by the transaction. In order to eliminate the false dependencies on such events, we can consider only the item versions read by t in computing T D t , as described in our earlier work on CSI [26]. However, for simplicity of discussion we omit this part in our protocol description.…”

Section: A Execution Of Local Transactionsmentioning

confidence: 99%

Transaction Management Using Causal Snapshot Isolation in Partially Replicated Databases

Padhye

Rajappan

Tripathi

2014

2014 IEEE 33rd International Symposium on Reliable Distributed Systems

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We present here a transaction management protocol using causal snapshot isolation in partially replicated multiversion databases. We consider here replicated databases consisting of multiple disjoint data partitions. A partition is not required to be replicated at all database sites, and a site may contain replicas for any number of partitions. Transactions can execute at any site and read or write data from any subset of the partitions, and its updates are propagated asynchronously to other sites. The protocol ensures that the snapshot observed by a transaction contains data versions that are causally consistent. The protocol requires propagating updates only to the sites replicating the updated items. In developing this protocol, we address the issues that are unique in supporting transactions with causal consistency together with the snapshot isolation model in partially replicated databases. Through experimental evaluations, we demonstrate the scalability of this model and its performance benefits over full replication models.

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Data mining-based hierarchical transaction model for multi-level consistency management in large-scale replicated databases

Mukherjee

Chaki

2021

Computer Standards & Interfaces

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Causally Coordinated Snapshot Isolation for Geographically Replicated Data

Cited by 7 publications

References 9 publications

A transaction model for management of replicated data with multiple consistency levels

A transaction model for management of replicated data with multiple consistency levels

Transaction Management Using Causal Snapshot Isolation in Partially Replicated Databases

Data mining-based hierarchical transaction model for multi-level consistency management in large-scale replicated databases

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