With the explosive growth of data-intensive mobile, social, commercial and industrial
applications, geo-distributed cloud becomes the main trend of cloud computing due to its …

The emergence of new classes of HPC applications and usage models, such as real-time
HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC …

Based on the problem of task communication overhead being higher than the task execution
time has a direct negative impact on the makespan of task scheduling in the current …

The Multiprocessor System-on-Chip (MPSoC) computing architectures are widely adopted
in modern embedded systems for real-time applications due to their high performance …

Large-scale distributed systems have advantages of high processing speeds and large
communication bandwidths over the network. The processing of huge real-world data …

Dynamic Task Scheduling is an enticing programming model aiming to ease the
development of parallel programs with intrinsically irregular or data-dependent parallelism …

Scientific workflow applications are deployed to run extensive volumes of data and to
manage comprehensive observations and simulations. They are resource-intensive and …

The moldable parallel task (MPT) is a kind of parallel task that their sub-tasks hold the
resources exclusively, which has been widely used in different areas. Our paper focuses on …

Edge computing emerged, shifting the data processing from the cloud to the edge network.
Edge computing leverages container orchestration tools such as Kubernetes to distribute …

Efficient workflow scheduling plays a critical role in achieving high performance in
heterogeneous distributed computing systems. Given its key importance, workflow …