Automating resources discovery for multiple data stores cloud applications

The production of huge amount of data and the emergence of cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, multidata store applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, developers need to master and deal with the complex processes of cloud discovery, and application deployment and execution. Moreover, the execution of join queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation effort. In this paper we propose a declarative approach enabling to lighten the burden of the tedious and non-standard tasks of discovering relevant cloud environment and deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and is currently used to implement use cases from the OpenPaaS project.