Workflow scheduling is one of the prominent issues in cloud computing which is aimed at
complete execution of workflows by considering their QoS requirements such as deadline …

Mobile-edge computing (MEC) is widely envisioned as a promising technique for
provisioning artificial intelligence (AI) capability for resource-limited Internet of Things (IoT) …

Scientific workflows, like other applications, benefit from the cloud computing, which offers
access to virtually unlimited resources provisioned elastically on demand. In order to …

Dynamic voltage and frequency scaling (DVFS) is a well-known energy consumption
optimization technique in embedded systems and dynamically scaling down the voltage of a …

Multiprocessor systems are increasingly deployed in real-time applications, where reliability,
energy consumption, and makespan are often the main scheduling objectives. In this work …

High energy consumption is one of the biggest obstacles to the rapid development of
computing systems, and reducing energy consumption is quite urgent and necessary for …

Heterogeneous processors are increasingly being used in embedded systems where
parallel applications with precedence-constrained tasks widely exist. Reliability is an …

Distributed computing workflow is an effective paradigm to express a range of applications
with cloud computing platforms for scientific research explorations. One of the most difficult …

One of the most challenging issues in cloud computing is workflow scheduling. Workflow
applications have a complex structure and many discrete tasks. Each task may include …

Nowadays, more and more workflow applications with different computing requirements are
migrated to clouds and executed with cloud resources. Workflow scheduling becomes a …