Cloud computing can provide virtually unlimited scalable high-performance computing resources. Cloud workflows often underlie many large-scale data and computation-intensive e-science applications such as earthquake modeling, weather forecasting, and astrophysics. During application modeling, these sophisticated processes are redesigned as cloud workflows, and at runtime, the models are executed by employing the supercomputing and data sharing capability of the underlying cloud computing infrastructures. Temporal QoS management in scientific cloud workflow systems focuses on real-world scientific applications that often must be completed by satisfying a set of temporal constraints such as milestones and deadlines. Meanwhile, activity duration, as a measurement of system performance, often needs to be monitored and controlled. This book demonstrates how to guarantee on-time completion of most tasks.