Chen, C.Chen, G.Jiang, D.Ooi, B.C.Vo, H.T.Wu, S.Xu, Q.COMPUTER SCIENCE2013-07-042013-07-042010Chen, C.,Chen, G.,Jiang, D.,Ooi, B.C.,Vo, H.T.,Wu, S.,Xu, Q. (2010). Providing scalable database services on the cloud. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6488 LNCS : 1-19. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-17616-6_1" target="_blank">https://doi.org/10.1007/978-3-642-17616-6_1</a>364217615103029743https://scholarbank.nus.edu.sg/handle/10635/40021The Cloud is fast gaining popularity as a platform for deploying Software as a Service (SaaS) applications. In principle, the Cloud provides unlimited compute resources, enabling deployed services to scale seamlessly. Moreover, the pay-as-you-go model in the Cloud reduces the maintenance overhead of the applications. Given the advantages of the Cloud, it is attractive to migrate existing software to this new platform. However, challenges remain as most software applications need to be redesigned to embrace the Cloud. In this paper, we present an overview of our current on-going work in developing epiC - an elastic and efficient power-aware data-intensive Cloud system. We discuss the design issues and the implementation of epiC's storage system and processing engine. The storage system and the processing engine are loosely coupled, and have been designed to handle two types of workload simultaneously, namely data-intensive analytical jobs and online transactions (commonly referred as OLAP and OLTP respectively). The processing of large-scale analytical jobs in epiC adopts a phase-based processing strategy, which provides a fine-grained fault tolerance, while the processing of queries adopts indexing and filter-and-refine strategies. © 2010 Springer-Verlag Berlin Heidelberg.Conference PaperNOT_IN_WOS