To support the large and various applications generated by the Internet of Things (IoT), Fog
Computing was introduced to complement the Cloud Computing and offer Cloud-like …

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 …

Modern science often requires the execution of large-scale, multi-stage simulation and data
analysis pipelines to enable the study of complex systems. The amount of computation and …

Edge computing is an emerging promising computing paradigm that brings computation and
storage resources to the network edge, hence significantly reducing the service latency and …

Workflow scheduling is a key issue and remains a challenging problem in cloud computing.
Faced with the large number of virtual machine (VM) types offered by cloud providers, cloud …

The advent of Cloud computing as a new model of service provisioning in distributed
systems encourages researchers to investigate its benefits and drawbacks on executing …

Executing complex, burst-parallel, directed acyclic graph (DAG) jobs poses a major
challenge for serverless execution frameworks, which will need to rapidly scale and …

On parallel and distributed heterogeneous computing systems, a heuristic-based task
scheduling algorithm typically consists of two phases: task prioritization and processor …

With the proliferation of versatile mobile applications, offloading compute-intensive tasks to
the MEC/Cloud becomes a dramatic technique due to the limited resources and high user …

We present a new cluster scheduler, GRAPHENE, aimed at jobs that have a complex
dependency structure and heterogeneous resource demands. Relaxing either of these …