The deployment of datasets in the heterogeneous edge-cloud computing paradigm has
received increasing attention in state-of-the-art research. However, due to their large sizes …

In Industry 4.0 and Internet of Things (IoT) environments, the heterogeneous edge-cloud
computing paradigm can provide a more proper solution to deploy scientific workflows …

Cloud computing can provide a more cost-effective way to deploy scientific workflows than
traditional distributed computing environments such as cluster and grid. Due to the large …

Compared to traditional distributed computing environments such as grids, cloud computing
provides a more cost-effective way to deploy scientific workflows. Each task of a scientific …

Scientific workflow processing in geo-distributed cloud is crucial for the scheduling of large-
scale tasks and the massive data placement among tasks. However, the task execution time …

Scientific cloud workflows enable the access to distributed computing resources in cloud
environments for executing scientific computing applications. In the literature, most workflow …

Scientific workflows are appropriate for treating large volumes of data and leading large-
scale experiments. They are time-consuming and resource-intensive applications that profit …

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 …

An important challenge for the adoption of cloud computing in the scientific community
remains the efficient allocation and execution of data-intensive scientific workflows to reduce …

The convergence of edge and cloud computing shares their strengths, such as unlimited
shared storage and computing resources from cloud, low-latency data preprocessing of …