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A dataflow runtime environment and static scheduler for edge, fog and in-situ computing

2019; Inderscience Publishers; Volume: 10; Issue: 3 Linguagem: Inglês

10.1504/ijguc.2019.099685

1741-8488

Caio B.G. Carvalho, Victor C. Ferreira, Felipe M. G. França, Cristiana Bentes, Gabriele Mencagli, Tiago A. O. Alves, Alexandre C. Sena, Leandro A. J. Marzulo,

Parallel Computing and Optimization Techniques

In the dataflow computation model, tasks are executed according to data dependencies, instead of following program order, enabling natural parallelism exploitation. Sucuri is a dataflow library for Python that allows transparent execution of applications on clusters of multicores, while taking care of scheduling issues. Recent trends in edge/fog/In-situ computing assume that storage and network devices will have processing elements with lower power consumption and performance, which would make a good case for runtime environments that deal with the data versus computation movements trade-off in a more transparent and automated way. This work presents a study on different factors that should be considered when running dataflow applications in in-situ environments, using Sucuri to conduct experiments in a small system emulating a smart storage (in-situ device) utilisation. A static scheduling solution is also presented, allowing Sucuri to choose the most suited approach regarding this in-situ trade-off.