A comparative study of scheduling algorithms for the multiple deadline-constrained workflows in heterogeneous computing systems with time windows

Scheduling tasks with precedence constraints on a set of resources with different performances is a well-known NP-complete problem, and a number of effective heuristics has been proposed to solve it. If the start time and the deadline for each specific workflow are known (for example, if a workflow starts execution according to periodic data coming from the sensors, and its execution should be completed before data acquisition), the problem of multiple deadline-constrained workflows scheduling arises. Taking into account that resource providers can give only restricted access to their computational capabilities, we consider the case when resources are partially available for workflow execution. To address the problem described above, we study the scheduling of deadline-constrained scientific workflows in non-dedicated heterogeneous environment. In this paper, we introduce three scheduling algorithms for mapping the tasks of multiple workflows with different deadlines on the static set of resources with previously known free time windows. Simulation experiments show that scheduling strategies based on a proposed staged scheme give better results than merge-based approach considering all workflows at once.