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 …

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services
according to a pay-per-use model. If users intend to run their workflow applications on cloud …

With the increasing popularity of Internet of Things (IoT), edge computing has become the
key driving force to provide computing resources, storage and network services closer to the …

As more people utilize the cloud, more employment opportunities become available. With
constraints such as a limited make-span, a high utilization rate of available resources …

Many scientific applications can be well modelled as large-scale workflows. Cloud
computing has become a suitable platform for hosting and executing them. Workflow …

With the increasing large number of cloud users, the number of tasks is growing
exponentially. Scheduling and balancing these tasks amongst different heterogeneous …

The concept of “extreme data” is a recent re-incarnation of the “big data” problem, which is
distinguished by the massive amounts of information that must be analyzed with strict time …

As the cost-driven public cloud services emerge, budget constraint is one of the primary
design issues in large-scale scientific applications executed on heterogeneous cloud …

In recent years, more and more large-scale data processing and computing workflow
applications run on heterogeneous clouds. Such cloud applications with precedence …

Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the
main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient …