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With the recent development of commercial Cloud offers, Cloud solutions are today the obvious solution for many computing use-cases. However, high performance scientific computing is still among the few domains where Cloud still raises more issues than it solves. Notably, combining the workflow representation of complex scientific applications with the dynamic allocation of resources in a Cloud environment is still a major challenge. In the meantime, users with monolithic applications are facing challenges when trying to move from classical HPC hardware to elastic platforms. In this paper, we present the structure of an autonomous workflow manager dedicated to IaaS-based Clouds (Infrastructure as a Service) with DaaS storage services (Data as a Service). The solution proposed in this paper fully handles the execution of multiple workflows on a dynamically allocated shared platform. As a proof of concept we validate our solution through a biologic application with the WASABI workflow.

A Cloud-aware Autonomous Workflow Engine and Its Application to Gene Regulatory Networks Inference