A wholistic optimization of containerized workflow scheduling and deployment in the cloud–edge environment

Abstract

Digital Twin in Industry 4.0 utilizes Internet of Things (IoT) to collect real-life data and combine it with simulation models for product design and development. The simulation process can be executed as a workflow, consisting of tasks with precedence constraints. In a container-based workflow execution system, each task in the workflow is executed in a container within a virtual machine (VM). In this paper, a three-step scheduling model is proposed to combine scheduling of container-based workflows and the deployment of containers on a cloud–edge environment. In the first step, virtual CPU (vCPU) is allocated for each container to enable vCPU sharing among different containers. Next, two-step resource deployment is used to schedule the containers onto VM, and VM onto the physical machines in either edge or cloud environment. Multiple objectives are considered, including minimizing makespan, load imbalance, and energy consumption, from the perspective of cloud–edge resources as well as containerized workflows. To obtain a set of non-dominated solutions, three evolution strategies are designed and combined with two multi-objective algorithm frameworks — co-evolution strategy (CES), basic non-co-evolution strategy (B-NCS), and hybrid non-co-evolution strategy (H-NCS). Simulation results demonstrate that the proposed model outperforms the existing two-step scheduling model and H-NCS performs better than other strategies.