Developing a Workflow Management System Simulation for Capturing Internal IaaS Behavioural Knowledge

Scientific workflows are becoming increasingly important for complex scientific applications. Conducting real experiments for large-scale workflows is challenging because they are very expensive and time consuming. A simulation is an alternative approach to a real experiment that can help evaluating the performance of workflow management systems (WMS) and optimise workflow management techniques. Although there are several workflow simulators available today, they are often user-oriented and treat the cloud as a black box. Unfortunately, this behaviour prevents the evaluation of the infrastructure level impact of the various decisions made by the WMSs. To address these issues, we have developed a WMS simulator (called DISSECT-CF-WMS) on DISSECT-CF that exposes the internal details of cloud infrastructures. DISSECT-CF-WMS enables better energy awareness by allowing the study of schedulers for physical machines. It also enables dynamic provisioning to meet the resource needs of the workflow application while considering the provisioning delay of a VM in the cloud. We evaluated our simulation extension by running several workflow applications on a given infrastructure. The experimental results show that we can investigate different schedulers for physical machines on different numbers of virtual machines to reduce energy consumption. The experiments also show that DISSECT-CF-WMS is up to 295× faster than WorkflowSim and still provides equivalent results. The experimental results of auto-scaling show that it can optimise makespan, energy consumption and VM utilisation in contrast to static VM provisioning.

  @article{al2023developing,
  title={Developing a Workflow Management System Simulation for Capturing Internal IaaS Behavioural Knowledge},
  author={Al-Haboobi, Ali and Kecskemeti, Gabor},
  journal={Journal of Grid Computing},
  volume={21},
  number={1},
  pages={2},
  year={2023},
  publisher={Springer}
}