Heuristic Dynamic Workflow Scheduling
Workshop: WORKS 2017 (12th Workshop on Workflows in Support of Large-Scale Science)
Authors: Kris Bubendorfer (Victoria University of Wellington)
Abstract: The advantages of cloud computing including elastic,
on demand, and pay per use instances, provide an ideal model
for resourcing large scale state-of-the-art scientific analyses.
Large scale scientific experiments are typically represented as
workflows and are the common model for characterizing escience
experiments and data analytics. Hosting and managing
scientific applications on the cloud poses new challenges in terms
of workflow scheduling which is key in leveraging cloud benefits.
Prior research has studied static scheduling when the number
of workflows is known in advance and all are submitted at
the same time. However, in practice, a scheduler may have to
schedule an unpredictable stream of workflows. In this paper, we
present a new algorithm, Dynamic Workload Scheduler (DWS).
Our algorithm addresses scheduling of multiple workflows with
the aim of satisfying the deadline for each workflow in a typical
cloud environment in which workflows can be submitted at any
time. Our results show that the DWS algorithm achieves an
average 10% higher success rate in terms of fulfilling deadlines
for different workloads and reduces the overall cost by an average
23% when compared to the most recent comparable algorithm.