With the development of cloud computing technologies and the multiplication of enterprise architecture reference frameworks, the industrials are encouraged to take into account the new concepts of cloud service in the evolution of their information system strategies. The present paper starts by presenting background concepts and the state of the art of the enterprise architecture, in order to propose a maturity model that helps enterprises to build and evaluate a functional component of their Information System architecture and allow them to qualify its externalization to the Cloud Computing as well as its monitoring.

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Software as a Service cloud computing model favorites the Multi-Tenancy as a key factor to exploit economies of scale. However Multi-Tenancy present several disadvantages. Therein, our approach comes to optimize instances assigned to multi-tenants with a solution using rich-variant components while ensuring more economies of scale and avoiding tenants hesitation to share resources. The paper present the theoretical and pragmatic cases of a user-aware multi-tenancy SaaS approach focused on graph-based algorithms. The theoretical case consists in having a set of tenants while the pragmatic case consists in adding a new tenants to a set of tenants.

Software as a Service cloud computing model favorites the Multi-Tenancy as a key factor to exploit economies of scale. However Multi-Tenancy present several disadvantages. Therein, our approach comes to assign instances to multi-tenants with an optimal solution while ensuring more economies of scale and avoiding tenants hesitation to share resources. The present paper present the architecture of our user-aware multi-tenancy SaaS approach based on the use of rich-variant components. The proposed approach seek to model services functional customization as well as automation of computing the optimal distribution of instances by tenants. The proposed model takes into consideration tenants functional requirements and tenants deployment requirements to deduce an optimal distribution using essentially a specific variability engine and a graph-based execution framework.

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