Enhanced Workload Driven Partitioning For Scalable Transactions Using Column Family No Sql Cloud Data Store

Abstract

Cloud Computing is a successful paradigm for deploying scalable and highly expected to be scalable and consistent. Data partitioning is a commonly used web applications at low cost. Traditional horizontal partitioning techniques are not capable of tracking the data access patterns of web applications. The development of novel, scalable workload-driven data partitioning is a requirement for improving scalability. To develop a consistent and scalable data management has become the vision for number of researchers related to the field of database from some years. Huge amount of data is generated by modern web applications. DBMS (Database Management System) plays a significant role for the management of huge amount of data. For the maintenance of reasonable and consistent performance, DBMS usually scale out the less cost. Usually, the relational database cannot be scaled out to less cost commodity servers. This has lead to NoSQL data stores birth. The utilization of data partitioning is a technique for the performance of scale out operation. For E-commerce applications, usually warehouse fulfils the requirements of the customers. In case of scalable workload driven partitioning method, the transactions logs are examined and the monitoring of data access patterns is taken place with the movement of periodic data. On the basis of data movement, the partitions are developed that did not stay perpetually. Though, the traditional partitioning algorithms did not work properly when the data access pattern varies and also cannot execute real world applications scenarios of e-commerce. Therefore, there is a requirement for the development of partitioning scheme for the improvement of scalability. Varied partitioning techniques such as, horizontal partitioning, vertical partitioning, and Workload Driven partitioning are studied and analyzed and an enhanced workload driven partitioning has been developed. Workload driven partitioning method is usually constructed on the basis of data applications according to the data access