Software as a Service (SaaS) is a new delivery model for software. Software in a SaaS model is no longer run exclusively for one customer at a customer's premise but run at a service provider and accessed via the Internet. A provider of Software as a Service exploits economies of scale by hosting and providing the same application for several different customers. However, each individual customer has different requirements for the same basic application. In order to allow each customer to customize the process layer and related artifacts of a SaaS application to their specific needs the application needs to provide a set of variability points that can be modified by customers. In this paper we describe the notion of a variability descriptor that defines variability points for the process layer and related artifacts of process-based, service-oriented SaaS applications. Furthermore we describe how these variability descriptors can be transformed into a WS-BPEL process model that can then be used to guide a customer through the customization of the SaaS application.
In this paper we describe a method and corresponding tool chain that allows moving an application to the cloud. In particular, we support to split an application such that various parts of it are moved to different clouds. This split can be done manually or by support of optimization algorithms. The split application is then automatically provisioned in the different target clouds. A metamodel for such applications supporting the proposed method is introduced. The architecture of a supporting tool is described. Experiences from the usage of the proposed method are reported.
Software as a service (SaaS) providers exploit economies of scale by offering the same instance of an application to multiple customers typically in a single-instance multitenant architecture model. Therefore the applications must be scalable, multi-tenant aware and configurable. In this paper we show how the services in a service-oriented SaaS application can be deployed using different multi-tenancy patterns. We describe how the chosen patterns influence the customizability, multi-tenant awareness and scalability of the application. Using the patterns we describe how individual services in a multitenant aware application can be not multi-tenant aware while maintaining the overall multi-tenant awareness of the application. We show based on a real-world example how the patterns can be used in practice and show how existing applications already use these patterns.
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