Performance of Scalable Data Stores in Cloud
Pankaj Deep Kaur1, Gitanjali Sharma2

1Pankaj Deep Kaur, Computer Science and Engineering, Guru Nanak Dev University- Regional Campus, Jalandhar (Panjab), India.
2Gitanjali Sharma, Computer Science and Engineering, Guru Nanak Dev University- Regional Campus, Jalandhar (Panjab), India.

Manuscript received on 15 June 2015 | Revised Manuscript received on 25 June 2015 | Manuscript Published on 30 June 2015 | PP: 212-216 | Volume-4 Issue-5, June 2015 | Retrieval Number: E4128064515/15©BEIESP
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Abstract: Cloud computing has pervasively transformed the way applications utilized underlying infrastructure like systems and software. System designers are in fast track pursuit of deploying applications/services over cloud to benefit from its elastic, scalable and pay-as-you-go model. Owing to the fact that many applications on cloud are extensively data driven, data management systems, hosting these applications, embody a vital component in cloud software store. However, maintaining performance of database read/write operations under fluctuating workloads, both regionally and globally, is quite challenging. In this context, distributed scalable data stores in cloud have promised high performance and reliable services through rapid partitioning, replication, elasticity and automated manager for self-management. Thus, the success of cloud computing paradigm critically depends on scalable, elastic and automated DBMSs. This paper discusses state-of-art of techniques and technologies utilized for cloud databases. It presents concepts of partitioning, replication, elastic scalability and automatic manager for management. The paper also addresses challenges faced by DBMSs designers.
Keywords: Amdahl’s Law, Elasticity, Scalability.

Scope of the Article: Cloud Computing