Few years ago, there was a huge development of new parallel and distributed systems. Due to many reasons, such as the inherent heterogeneity, the diversity, and the continuous evolution of such computational supports, it is very hard to solve efficiently a target problem by using a single algorithm or to write portable programs that perform well on any architecture. Toward this goal, we propose a generic framework combining communication models and adaptive approaches to deal with the performance modeling problem associated to the design of efficient parallel algorithms on grid computing environments, and we apply this methodology on collective communication operations. Experiments performed on a grid platform prove that the framework provides significant performances while determining the best combination model-algorithm depending on the problem and architecture parameters.