Grids and clouds are utilized for the execution of applications composed of dependent tasks, usually modeled as workflows. To efficiently run the application, a scheduler must distribute the components of the workflow in the available resources using information about duration of tasks and communication between tasks in the workflow. However, such information may be subject to imprecisions, thus not reflecting what is observed during the execution. In this paper we propose a simple way of representing the costs of the components in a workflow in order to reduce the impact of uncertainties introduced by wrong estimations, and also to ease the application specification for the user. Evaluation shows that the use of relative costs in tasks and dependencies can improve in many cases the resulting schedule when compared to cases where the input data carries an uncertainty of 20% and 50%.
For example, Bittencourt et al. REF proposed an adaptive strategy to reduce the impact of uncertainty introduced by wrong estimations by using relative costs in the input data for both processing and communication times.
Using relative costs in workflow scheduling to cope with input data uncertainty
MGC '12
Computer Science