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Advisor(s)
Abstract(s)
Workflows have been successfully applied to express the decomposition of complex scientific applications. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from tasks specification, decentralizing the control of workflow activities allowing their tasks to run in distributed infrastructures, and supporting dynamic workflow reconfigurations. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on Process Networks, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures. Each AWA executes a task developed as a Java class with a generic interface allowing end-users to code their applications without low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables dynamic workflow reconfiguration. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to the Amazon (Elastic Computing EC2) Cloud.
Description
Keywords
Scientific workflows Parallel execution Distributed processing Cloud Tuple space
Citation
ASSUNÇÃO, Luís; GONÇALVES, Carlos; CUNHA, José C. – Autonomic activities in the execution of scientific workflows: evaluation of the AWARD framework. In 2012 IEEE 9th International Conference on Ubiquitous Intelligence and Computing (UIC) / IEEE 9th International Conference on Autonomic and Trusted Computing (ATC). Fukuoka, Japan: IEEE, 2012. ISBN 978-1-4673-3084-8. Pp. 423-430.
Publisher
IEEE