Browsing by Subject "Data Replication Management"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Scalable Transaction Management for Partially Replicated Data in Cloud Computing Environments
    (IEEE, 2016) Tripathi, Anand; Rajappan, Gowtham
    We present here a scalable protocol for transaction management in key-value based multi-version data storage systems supporting partial replication of data in cloud and cluster computing environments. We consider here systems in which the database is sharded into partitions, a partition is replicated only at a subset of the nodes in the system, and no node contains all partitions. The protocol presented here is based on the Partitioned Causal Snapshot Isolation (PCSI) model and it enhances the scalability of that model. The PCSI protocol is scalable for update transactions which involve updating of only local partitions. However, it faces scalability limitations when transactions update non-local partitions. This limitation stems from the scheme used for obtaining update timestamps for remote partitions, causing vector clocks to grow with the system configuration size. We present here a new protocol based on the notion of sequence number escrow and address the underlying technical problems. Our experimental evaluations show that this protocol scales out almost linearly when workloads involve transactions with remote partition updates. We present here the performance of this protocol for three different workloads with varying mix of transaction characteristics.
  • Thumbnail Image
    Item
    A Transaction Model for Management of Replicated Data with Multiple Consistency Levels
    (IEEE, 2016) Tripathi, Anand; Thirunavukarasu, BhagavathiDhass
    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.
  • Thumbnail Image
    Item
    A Transaction Model with Multilevel Consistency for Shared Data in Distributed Groupware Systems
    (IEEE, 2016) Tripathi, Anand
    In groupware systems a broad range of requirements for user coordination and data consistency need to be supported. The notions of event causality and user awareness are central in such requirements. Traditional transaction models supported in general purpose database management systems with strong consistency guarantees have been found to be unsuitable for groupware systems. Weaker models for data consistency are needed for user awareness and cooperative activities. Objects in the shared workspace need to be managed with different consistency guarantees. Towards such requirements, we examine here the applicability of a distributed transaction management model which supports multilevel consistency. The consistency levels supported in this model 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. We review the coordination and data consistency requirements in groupware systems. We show using two examples how replicated shared data in distributed groupware systems can be managed with multiple consistency levels using this model.