Many techniques have been proposed to scale web applications. However, the data interdependencies between the database queries and transactions issued by the applications limit their efficiency. We claim that major scalability improvements can be gained by restructuring the web application data into multiple independent data services with exclusive access to their private data store. While this restructuring does not provide performance gains by itself, the implied simplification of each database workload allows a much more efficient use of classical techniques. We illustrate the data denormalization process on three benchmark applications: TPC-W, RUBiS and RUBBoS. We deploy the resulting service-oriented implementation of TPC-W across an 85-node cluster and show that restructuring its data can provide at least an order of magnitude improvement in the maximum sustainable throughput compared to master-slave database replication, while preserving strong consistency and transactional properties.

Abstract—On a such wide-area environment as a Grid, data placement is an important aspect of distributed database systems. In this paper, we address the problem of initial placement of database no-replicated fragments in Grid architecture. We propose a graph based approach that considers resource restrictions. The goal is to optimize the use of computing, storage and communication resources. The proposed approach is developed in two phases: in the first phase, we perform fragment grouping using knowledge about fragments dependency and, in the second phase, we determine an efficient placement of the fragment groups on the Grid. We also show, via experimental analysis that our approach gives solutions that are close to being optimal for different databases and Grid configurations. Keywords—Grid computing, Distributed systems, Data resources management, Database systems, Database placement.

The purpose of this paper is to present an introduction to Distributed Databases which are becoming very popular now a days. Today’s business environment has an increasing need for distributed database and Client/server applications as the desire for reliable, scalable and accessible information is Steadily rising. Distributed database systems provide an improvement on communication and data processing due to its data distribution throughout different network sites. Not Only is data access faster, but a single-point of failure is less likely to occur, and it provides local control of data for users.

Abstract- As many distributed database applications contain online information that change continuously and expand incrementally, comprehensive cloud Application Programming Interface API’s are required to monitor and control the accuracy of the information and data proliferation. This cloud software is required to monitor and control the accuracy of the information and data proliferation. This software can be viewed as integrated cloud computing services; data fragmentation, clustering network sites, and fragments Allocation that support transactional database applications. In this paper, we describe our data Fragmentation as a Service (FaaS) in construction of a cloud computing software system. Specifically, we design a novel data fragmentation as a service to facilitate enormous data processing, and introduce some functioning enhancement on data distribution to improve the cloud system performance. This research presents our attempt to implement data fragmentation service in a cloud computing system, with large scale data mining as targeted application.

Abstract- In this paper, Neural Network approach is proposed for fragmenting and defragmenting the distributed database for Aditya Educational groups in Andhra Pradesh. Collaboration of different colleges like Engineering, Pharmacy, PG exists. Hence, we need a distributed database environment for designing sub databases and fragmenting them on the sites which are geographically separated. All the fragments in return should be reconstructed as one database. Therefore different techniques are considered for a database fragmentation. A multi-layered neural network architecture is used to implement the fragmentation and defragmentation.