Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms

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. Emerging Workflow as a Service (WaaS) platforms offer scientists a simple, easily accessible, and cost-effective way of deploying their applications in the cloud at anytime and from anywhere. They are multi-tenant frameworks and are designed to manage the execution of a continuous workload of heterogeneous workflows. To achieve this, they leverage the compute, storage, and network resources offered by Infrastructure as a Service (IaaS) providers. Hence, at any given..