Abstract

Recent advances in cloud systems, on-demand circuits and software-defined networking have created new opportunities to enable complex, data-intensive scientific applications to run on dynamic networked cloud infrastructures. In this work, we present an end-to-end framework for autonomic adaptation for scientific workflows on networked cloud systems, which leverages novel network provisioning technologies. We present an application-independent controller framework called Mobius++ that includes dynamic network adaptation capabilities using Software-Defined Networking (SDN) mechanisms, which enables workflow management systems to address competing priorities of workflow operations, data movements in particular. We use a representative, data-intensive bioinformatics workflow as a driving use case to showcase the above capabilities. Experimental results show that the Mobius++ framework, in …

Date

2017

Authors

Anirban Mandal, Paul Ruth, Ilya Baldin, Rafael Ferreira Da Silva, Ewa Deelman

Conference

2017 IEEE 13th International Conference on e-Science (e-Science)

Pages

566-575

Publisher

IEEE