Adaptive execution of scientific workflow applications on clouds

Many e-science applications can be modeled as workflow applications. In this programming model, scientific applications are described as a set of tasks that have dependencies between them. Clouds are natural candidates for hosting such applications. This is because some of their core characteristics, such as rapid elasticity, resource pooling, and pay per use, are well suited to the nature of scientific applications that experience variable demand, spikes in resource (i.e., of the central processing unit [CPU] or disk) utilization, and sometimes, urgency for generation of results. As current workflow management systems (WfMSs) cannot support efficient and automated execution of workflow in clouds that support adaptive execution, fault tolerance, and data privacy, in this chapter we detail the requirements of a WfMS that supports these requirements, its architecture, and an application scenario involving simulation of Singapore’s public transport system