Budget-driven scheduling of scientific workflows in IaaS clouds with fine-grained billing periods

Abstract

With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Scheduling algorithms are crucial in enabling the efficient automation of these large-scale workflows, and considerable effort has been made to develop novel heuristics tailored for the cloud resource model. The majority of these algorithms focus on coarse-grained billing periods that are much larger than the average execution time of individual tasks. Instead, our work focuses on emerging finer-grained pricing schemes (e.g., per-minute billing) that provide users w..