A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments

Abstract

Large-scale scientific problems are often modeled as workflows. The ever-growing data and compute requirements of these applications has led to extensive research on how to efficiently schedule and deploy them in distributed environments. The emergence of the latest distributed systems paradigm, cloud computing, brings with it tremendous opportunities to run scientific workflows at low costs without the need of owning any infrastructure. It provides a virtually infinite pool of resources that can be acquired, configured, and used as needed and are charged on a pay-per-use basis. However, along with these benefits come numerous challenges that need to be addressed to generate efficient schedu..