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eScience consists of computation-intensive workflows executing on highly distributed networks. Service compositions aggregate web services to automate scientific and enterprise business processes. Along with the increased demand for data quality and Quality of Service (QoS) for an accurate outcome in a shorter completion time, execution of the eScience workflows and service compositions are also required to be distributed efficiently across various geo-distributed nodes. This paper presents Mayan, a Software-Defined Networking (SDN) based approach for service composition.
Mayan i) facilitates an adaptive execution of scientific workflows, ii) offers a more efficient service composition by leveraging distributed execution frameworks, in addition to the traditional web service engines, and iii) enables a very large-scale reliable service composition by finding and consuming the current best-fit among the multiple implementations or deployments of the same service.