Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163299
Title: A wholistic optimization of containerized workflow scheduling and deployment in the cloud–edge environment
Authors: Li, Feng
Tan, Wen Jun
Cai, Wentong
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Li, F., Tan, W. J. & Cai, W. (2022). A wholistic optimization of containerized workflow scheduling and deployment in the cloud–edge environment. Simulation Modelling Practice and Theory, 118, 102521-. https://dx.doi.org/10.1016/j.simpat.2022.102521
Project: A19C1a0018
Journal: Simulation Modelling Practice and Theory
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.
URI: https://hdl.handle.net/10356/163299
ISSN: 1569-190X
DOI: 10.1016/j.simpat.2022.102521
Schools: School of Computer Science and Engineering 
Rights: © 2022 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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