DynAdapt: Alterações na Definição de Atividades de Workflows Científicos em Tempo de Execução

• Igor de Araújo dos Santos UFRJ
• Jonas Dias UFRJ
• Daniel de Oliveira UFRJ / CEFET-RJ
• Eduardo Ogasawara UFRJ
• Marta Mattoso UFRJ

Abstract

Scientific workflows can represent experiments based on computer simulations. Generally, workflows executed in parallel are time consuming and manage a large amount of data. Such characteristics may make the exploratory process of the experiment more difficult or very expensive. In this scenario, it is necessary to handle workflows with dynamic aspects, which allow for changes in workflow definitions during runtime. This way, this article proposes an approach to allow for changes in the definition of the activities of the workflow during runtime, according to criteria defined by scientists.

References

Boeres, C., Sardiña, I., Drummond, L., (2011), "An efficient weighted bi-objective scheduling algorithm for heterogeneous systems", Parallel Computing, v. 37, n. 8 (Agosto.), p. 349–364.

Clarence Ellis, Karim Keddara, Grzegorz Rozenberg, (1995), "Dynamic change within workflow systems". In: COCS ’95 Proceedings of conference on Organizational computing systems, p. 10 – 21, New York, NY, USA.

Costa, F., Silva, V., Oliveira, D., Ocana, K., Dias, J., Ogasawara, E., Mattoso, M., (2013), "Capturing and Querying Workflow Runtime Provenance with PROV: a Practical Approach". In: International Workshop on Managing and Querying Provenance Data at Scale (BigProv’13), Genova, Italy.

Deelman, E., Gannon, D., Shields, M., Taylor, I., (2009), "Workflows and e-Science: An overview of workflow system features and capabilities", Future Generation Computer Systems, v. 25, n. 5, p. 528–540.

Dias, J., Ogasawara, E., Oliveira, D., Porto, F., Coutinho, A., Mattoso, M., (2011), "Supporting Dynamic Parameter Sweep in Adaptive and User-Steered Workflow". In: 6th Workshop on Workflows in Support of Large-Scale Science, p. 31–36, Seattle, WA, USA.

Freire, J., Koop, D., Santos, E., Silva, C. T., (2008), "Provenance for Computational Tasks: A Survey", Computing in Science and Engineering, v.10, n. 3, p. 11–21.

Gil, Y., Deelman, E., Ellisman, M., Fahringer, T., Fox, G., Gannon, D., Goble, C., Livny, M., Moreau, L., Myers, J., (2007), "Examining the Challenges of Scientific Workflows", Computer, v. 40, n. 12, p. 24–32.

Kammer, P. J., Bolcer, G. A., Taylor, R. N., Hitomi, A. S., Bergman, M., "Techniques for Supporting Dynamic and Adaptive Workflow", Computer Supported Cooperative Work (CSCW), p. 269–292.

Moreau, L., Missier, P., Belhajjame, K., Cresswell, S., Golden, R., Groth, P., Miles, S., Sahoo, S., (2011). The PROV Data Model and Abstract Syntax Notation. Disponível em: http://www.w3.org/TR/prov-dm/. Acesso em: 14 Dec 2011.

Ocaña, K. A. C. S., Oliveira, D., Dias, J., Ogasawara, E., Mattoso, M., (2011), "Optimizing Phylogenetic Analysis Using SciHmm Cloud-based Scientific Workflow". In: IEEE e-Science 2011, p. 190–197, Stockholm, Sweden.

Ogasawara, E., Dias, J., Silva, V., Chirigati, F., Oliveira, D., Porto, F., Valduriez, P., Mattoso, M., (2013), "Chiron: A Parallel Engine for Algebraic Scientific Workflows", Concurrency and Computation Ogasawara, E., Paulino, C., Murta, L., Werner, C., Mattoso, M., (2009), "Experiment Line: Software Reuse in Scientific Workflows". In: SSDBM 2009, p. 264–272, New Orleans, Louisiana, USA.

Oliveira, D., Ocaña, K., Baião, F., Mattoso, M., (2012), "A Provenance-based Adaptive Scheduling Heuristic for Parallel Scientific Workflows in Clouds", Journal of Grid Computing, v. 10, n. 3, p. 521–552.

Tudruj, M., Kopanski, D., Borkowski, J., (2007), "Dynamic Workflow Control with Global States Monitoring". In: Parallel and Distributed Computing, 2007. ISPDC ’07. Sixth International Symposium on, p. 44, Hagenberg.