Abstract
The prominent characteristic of the workflow applications, i.e., complex dependencies among workflow tasks, have made workflow scheduling problem a challenging problem in any distributed computing paradigm such as cloud and grid computing. So far, various workflow scheduling strategies have been proposed whose usual assumption is that the parameters corresponding to workflow tasks, such as length and output size, are deterministic and known in advance. Whereas, due to uncertainties about loops and decision structures in task instructions, these parameters are never deterministic. Therefore, considering workflows as deterministic models, in order to prioritize interdependent tasks during mapping of tasks onto computational resources, will lead to an inefficient scheduling scheme. To cope with these uncertainties, we consider workflows as stochastic ones and model tasks parameters as normal random variables. But in order to simplify computation process, we approximate a stochastic workflow as several interval workflows. Eventually, we extend the traditional critical path algorithm and obtain more detailed ranking of the tasks. The simulation results show that thanks to this detailed ranking information, better decisions are made about assigning tasks to computational resources.
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Matani, A., Darvishy, A. (2019). A Novel Critical-Path Based Scheduling Algorithm for Stochastic Workflow in Distributed Computing Systems. In: Grandinetti, L., Mirtaheri, S., Shahbazian, R. (eds) High-Performance Computing and Big Data Analysis. TopHPC 2019. Communications in Computer and Information Science, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-33495-6_37
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DOI: https://doi.org/10.1007/978-3-030-33495-6_37
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