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There are many datastore systems to choose from that differ in many ways including public versus private cloud support, data management interfaces, programming languages, supported feature sets, fault tolerance, consistency guarantees, configuration, and their deployment processes. In this paper, we focus on technologies for structured data access (database/datastore systems) in cloud systems. Our goal is to simplify the use of datastore systems through automation and to facilitate their empirical evaluation using real world applications. To enable this, we provide a cloud platform abstraction layer that decouples a data access API from its implementation. Applications that use this API can use any datastore that “plugs into” our abstraction layer, thus enabling application portability. We use this layer to extend the functionality of multiple datastores without modifying the datastores directly. Specifically, we provide support for ACID transaction semantics for popular key-value stores (none of which provide this feature). We integrate this layer into the AppScale cloud platform—an open-source cloud platform that executes cloud applications written in Python, Java, and Go, over virtualized cluster resources and infrastructures-as-a-service (Eucalyptus, OpenStack, and Amazon EC2). We use this system to investigate the impact of extending disparate datastores via the application portability layer with distributed transaction support.

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Correspondence to Chandra Krintz.

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This paper is a combined and extended version of papers [5] and [12].

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Chohan, N., Bunch, C., Krintz, C. et al. Cloud Platform Datastore Support. J Grid Computing 11, 63–81 (2013).

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