In the last years, the availability and models of use of networked computing resources within reach of e-Science are rapidly changing and see the coexistence of many disparate paradigms: high-performance computing, grid, and recently cloud. Unfortunately, none of these paradigms is recognized as the ultimate solution, and a convergence of them all should be pursued. At the same time, recent works have proposed a number of models and tools to address the growing needs and expectations in the field of e-Science. In particular, they have shown the advantages and the feasibility of modeling e-Science environments and infrastructures according to the service-oriented architecture. In this paper, we suggest a model to promote the convergence and the integration of the different computing paradigms and infrastructures for the dynamic on-demand provisioning of resources from multiple providers as a cohesive aggregate, leveraging the service-oriented architecture. In addition, we propose a design aimed at endorsing a flexible, modular, workflow-based computing model for e-Science. The model is supplemented by a working prototype implementation together with a case study in the applicative domain of bioinformatics, which is used to validate the presented approach and to carry out some performance and scalability measurements. 1. Introduction In the last years, the availability and models of use of networked computing resources within reach of e-Science are rapidly changing and see the coexistence of many disparate paradigms, featuring their own characteristics, advantages, and limitations. Among the main paradigms, we find high-performance computing (HPC), grid, and cloud. In all cases, the objective is to best provide hardware and software resources to user applications with the help of schedulers, reservation systems, control interfaces, authentication mechanisms, and so on. At the same time, a number of works [1–4] have proposed a number of models and tools to address the growing needs and expectations in the field of e-Science. In particular, the works in [4, 5] have shown the advantages and the feasibility, but also the problems, of modeling e-Science environments and infrastructures according to the service-oriented architecture (SOA) and its enabling technologies such as web services (WS). Among the main advantages of such approach, we find interoperability, open standards, modularity, dynamic publish-find-bind, and programmatic access. A detailed comparison of the characteristics of HPC, grid, and cloud paradigms is presented in [6], where it is observed
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