Earticle

Data replication is a key factor to achieve scalability and fault tolerance in databases as it maintains several clones of data objects. A change made in data automatically triggers carrying out similar changes in each of the replica. A number of data replication techniques have been proposed in the contemporary literature due to its large scale application in the real world like astronomy, high energy physics and biology. In this study we provide a critical analysis of these techniques.

In real-time database systems, transactions's commitment done before the given deadlines is more important than just getting the maximum throughput. Transactions missing the given deadlines are no longer meaningful in real-time applications. Therefore, there is a need for new transaction processing models to meet the given deadlines in real-time database applications, because moat conventional transaction models are not designed to meet deadlines. In this paper we propose a new transaction models which uses multiple versions of a data item. The model uses read-from graphs and dynamic reorder of transactions to meet deadlines. A read-from graph contains the past read semantics of read operations and support the model to decide which database operation to be taken. Then, we show simulation results comparing the proposed model with other transaction models such as two phase locking model and the optimistic concurrency control model.