Cloud Computing is a successful paradigm for deploying scalable and highly available web applications at low cost. In real life scenarios, the applications are expected to be scalable and consistent. Data partitioning is a commonly used technique for improving scalability. 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. This paper proposes a novel workload-aware approach, with scalable workload-driven data partitioning based on data access patterns of web applications for transaction processing. It is specially designed to scale out using NoSQL data stores. In contrast to the existing static approaches, this approach offers high throughput, lower response time, and a less number of distributed transactions. Further, implementation and validation of scalable workload-driven partitioning scheme is carried out through experimentation over cloud data stores such as Hadoop HBase and Amazon SimpleDB. An experimental results of the concerned partitioning scheme is conducted using the industry standard TPC-C benchmark. Analytical and experimental results are observed and it shows that scalable workload-driven data partitioning outperforms the schema level and graph partitioning in terms of throughput, response time and distributed transactions.
['Swati Ahirrao', 'Rajesh Ingle']
Scalable transactions in cloud data stores