To program in distributed computing environments such as grids and clouds, workflow is
adopted as an attractive paradigm for its powerful ability in expressing a wide range of …

Cloud Service Providers are speedily becoming the target platform for scientific workflow
computations due to the massive possible and flexible pay-as-you-go pricing model …

Among scheduling algorithms of scientific workflows, the graph partitioning is a technique to
minimize data transfer between nodes or clusters. However, when the graph partitioning is …

In-situ workflows are a particular class of scientific workflows where different components
(such as simulation, visualization, machine learning, and data analysis) run concurrently. In …

The recent increased use of workflow management systems by large scientific
collaborations presents the challenge of scheduling large-scale workflows onto distributed …

Workflow scheduling in the cloud is the process of allocating tasks to limited cloud resources
to maximise resource utilization and minimise makespan. This is often achieved by adopting …

Cloud computing has emerged as a promising computational infrastructure for cost-efficient
workflow execution by provisioning on-demand resources in a pay-as-you-go manner. While …

Long-term execution of scientific applications often leads to dynamic workloads and varying
application requirements. When the execution uses resources provisioned from IaaS clouds …

Cloud computing is now a well-adopted computing paradigm. With unprecedented
scalability and flexibility, the computational cloud is able to carry out large scale computing …

In the modern era, workflows are adopted as a powerful and attractive paradigm for
expressing/solving a variety of applications like scientific, data intensive computing, and big …