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Swati Ahirrao
Computer Science Symbiosis Institute of Technology
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Data Partitioning in Mongo DB with Cloud Aakanksha Jumle; Swati Ahirrao
International Journal of Advances in Applied Sciences Vol 7, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.242 KB) | DOI: 10.11591/ijaas.v7.i1.pp21-28

Abstract

Cloud computing offers various and useful services like IAAS, PAAS SAAS for deploying the applications at low cost. Making it available anytime anywhere with the expectation to be it scalable and consistent. One of the technique to improve the scalability is Data partitioning. The alive techniques which are used are not that capable to track the data access pattern. This paper implements the scalable workload-driven technique for polishing the scalability of web applications. The experiments are carried out over cloud using NoSQL data store MongoDB to scale out. This approach offers low response time, high throughput and less number of distributed transaction. The results of partitioning technique is conducted and evaluated using TPC-C benchmark.
Co-Authors Aakanksha Jumle