Browsing by Subject "Database Transactions"

Now showing 1 - 3 of 3
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    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.
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    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.
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    A Transactional Model for Parallel Programming of Graph Applications on Computing Clusters
    (IEEE, 2017) Tripathi, Anand; Padhye, Vinit; Sunkara, Tara Sasank; Tucker, Jeremy; Thirunavukarasu, BhagavathiDhass; Pandey, Varun; Sharma, Rahul R.
    We present here the results of our investigation of a transactional model of parallel programming on cluster computing systems. This model is specifically targeted for graph applications with the goal of harnessing unstructured parallelism inherently present in many such problems. In this model, tasks for vertex-centric computations are executed optimistically in parallel as serializable transactions. A key-value based globally shared object store is implemented in the main memory of the cluster nodes for storing the graph data. Task computations read and modify data in the distributed global store, without any explicitly programmed message-passing in the application code. Based on this model we developed a framework for parallel programming of graph applications on computing clusters. We present here the programming abstractions provided by this framework and its architecture. Using several graph problems we illustrate the simplicity of the abstractions provided by this model. These problems include graph coloring, k-nearest neighbors, and single-source shortest path computation. We also illustrate how incremental computations can be supported by this programming model. Using these problems we evaluate the transactional programming model and the mechanisms provided by this framework.