A Clustering-Based Approach to Static Scheduling of Multiple Workflows with Soft Deadlines in Heterogeneous Distributed Systems

Typical patterns of using scientific workflow management systems (SWMS) include periodical executions of prebuilt workflows with precisely known estimates of tasks’ execution times. Combining such workflows into sets could sufficiently improve resulting schedules in terms of fairness and meeting users’ constraints. In this paper, we propose a clustering-based approach to static scheduling of multiple workflows with soft deadlines. This approach generalizes commonly used techniques of grouping and ordering of parts of different workflows. We introduce a new scheduling algorithm, MDW-C, for multiple workflows with soft deadlines and compare its effectiveness with task-based and workflow-based algorithms which we proposed earlier in [1]. Experiments with several types of synthetic and domain-specific test data sets showed the superiority of a mixed clustering scheme over task-based and workflow-based schemes. This was confirmed by an evaluation of proposed algorithms on a basis of the CLAVIRE workflow management platform