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Titolo: Extending the SOA paradigm to e-Science environments
Data di pubblicazione: 2011
Citazione: Extending the SOA paradigm to e-Science environments / BOSIN A; DESSÌ N; PES B. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 27(2011), pp. 20-31.
Abstract: In the business world, Service Oriented Architecture (SOA) has recently gained popularity as a new approach to integrate business applications. In a similar way, scientific workflows can accomplish the automation of large-scale e-Science applications. However, the use of workflows in scientific environments differs from that in business environments. Scientific workflows need to handle large amounts of data, deal with heterogeneous and distributed resources such as the Grid, and require specialized software applications that are written in diverse programming languages, most of which are not popular in business environments. In this paper, we analyze the preparedness and the shortcomings of the SOA paradigm in addressing the needs of e-Science and the extent to which this can be done. The paper identifies the characteristics of a Virtual Organization providing scientific services, and presents a model placing particular emphasis on BPEL processes as a mean for supporting the interaction with Web Services. We discuss the challenges encountered in the seamless integration of BPEL processes within an e-Science infrastructure and we propose a set of complementary infrastructural services. By providing business utilities and automation technology founded on Web Services, infrastructural services cooperate with BPEL in ensuring on-demand resource provisioning for the execution of scientific workflows, while addressing some critical issues such as security, access control and monitoring. Furthermore, the paper presents our experience in adopting the proposed approach within a collaborative environment for bioinformatics. To illustrate how a scientific experiment can be formalized and executed, we focus on micro-array data processing, a field that will be increasingly common in applications of machine learning to molecular biology.
Tipologia:1.1 Articolo in rivista

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