Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.21/4789
Título: Autonomic workflow activities: the award framework
Autor: Assunção, Luís Manuel da Costa
Gonçalves, Carlos Jorge de Sousa
Cunha, José C.
Palavras-chave: Autonomic Computing
Cloud
Parallel and Distributed Processing
Scientific Workflows
Tuple Space
Data: 2014
Editora: IGI Global
Citação: ASSUNÇÃO, Luís Manuel da Costa; GONÇALVES, Carlos Jorge de Sousa; CUNHA, José C. – Autonomic workflow activities: The award framework. International Journal of Adaptive, Resilient and Autonomic Systems. ISSN: 1947-9220. Vol. 5, nr. 2 (2014) p. 57-82.
Resumo: Workflows have been successfully applied to express the decomposition of complex scientific applications. This has motivated many initiatives that have been developing scientific workflow tools. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from workflow tasks specification, decentralizing the control of workflow activities, and allowing their tasks to run autonomous in distributed infrastructures, for instance on Clouds. Furthermore many workflow tools only support the execution of Direct Acyclic Graphs (DAG) without the concept of iterations, where activities are executed millions of iterations during long periods of time and supporting dynamic workflow reconfigurations after certain iteration. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on the Process Networks model, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures, e. g. on Clouds. Each AWA executes a Task developed as a Java class that implements a generic interface allowing end-users to code their applications without concerns for low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables support to dynamic workflow reconfiguration and monitoring of the execution of workflows. We describe how AWARD supports dynamic reconfiguration and discuss typical workflow reconfiguration scenarios. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to a small dedicated cluster and the Amazon (Elastic Computing EC2) Cloud.
Peer review: yes
URI: http://hdl.handle.net/10400.21/4789
DOI: 10.4018/ijaras.2014040104
ISSN: 1947-9220
1947-9239
Aparece nas colecções:ISEL - Eng. Elect. Tel. Comp. - Artigos

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