  As Kubernetes becomes the infrastructure of the cloud-native era, the
integration of workflow systems with Kubernetes is gaining more and more
popularity. To our knowledge, workflow systems employ scheduling algorithms
that optimize task execution order of workflow to improve performance and
execution efficiency. However, due to its inherent scheduling mechanism,
Kubernetes does not execute containerized scheduling following the optimized
task execution order of workflow amid migrating workflow systems to the
Kubernetes platform. This inconsistency in task scheduling order seriously
degrades the efficiency of workflow execution and brings numerous challenges to
the containerized process of workflow systems on Kubernetes. In this paper, we
propose a cloud-native workflow engine, also known as KubeAdaptor, a docking
framework able to implement workflow containerization on Kubernetes, integrate
workflow systems with Kubernetes, ensuring the consistency of task scheduling
order. We introduce the design and architecture of the KubeAdaptor, elaborate
on the functionality implementation and the event-trigger mechanism within the
KubeAdaptor. Experimental results about four real-world workflows show that the
KubeAdaptor ensures the consistency of the workflow systems and Kubernetes in
the task scheduling order. Compared with the baseline Argo workflow engine, the
KubeAdaptor achieves better performance in terms of the average execution time
of task pod, average workflow lifecycle, and resource usage rate.
