KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration Framework

As more Internet of Things (IoT) applications gradually move toward the cloud-edge collaborative model, the containerized scheduling of workflows extends from the cloud to the edge. However, given the high delay of the communication network, loose coupling of structure, and resource heterogeneity between the cloud and the edge, workflow containerization scheduling in the cloud-edge scenarios faces the difficulty of resource collaboration and application collaboration management. To address these two issues, we propose a KubeEdge-cloud–edge-scheduling scheme named KCES. This workflow containerization scheduling scheme includes a cloud-edge workflow scheduling engine for KubeEdge and incorporates workflow scheduling strategies for tasks’ horizontal roaming and vertical offloading. This article proposes a cloud-edge workflow scheduling model and node model, as well as a workflow scheduling engine designed to maximize cloud-edge resource utilization under the constraint of workflow task delay. A cloud-edge resource hybrid management technology is used to devise the cloud-edge resource evaluation and resource allocation algorithms to achieve cloud-edge resource collaboration. Based on the ideas of distributed functional roles and the hierarchical division of computing power, the horizontal roaming among the edges and cloud-edge vertical offloading strategies for workflow tasks are designed to realize cloud-edge application collaboration. Experimental results using a customized IoT application workflow instance demonstrate that KCES outperforms three comparing algorithms in total workflow time, average workflow time, and resource usage and features horizontal roaming and vertical offloading of workflow tasks.

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Publication Details

Journal
IEEE Internet of Things Journal
Published
2024-09-23
DOI
https://doi.org/10.1109/jiot.2024.3466231
Citations
12
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
9.35

Funders

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article

KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration Framework

Qinghua Han, Chenggang Shan, Runze Gao, Yuanqing Xia et al.
12 citations
IEEE Internet of Things Journal
Cloud Computing and Resource Management
9.35
article

KCES: A Workflow Containerization Scheduling Scheme Under Cloud-Edge Collaboration Framework

Qinghua Han, Chenggang Shan, Runze Gao, Yuanqing Xia, Zhen Yang, Tian Liu, Jinhui Zhang
article en
12 citations

Abstract

As more Internet of Things (IoT) applications gradually move toward the cloud-edge collaborative model, the containerized scheduling of workflows extends from the cloud to the edge. However, given the high delay of the communication network, loose coupling of structure, and resource heterogeneity between the cloud and the edge, workflow containerization scheduling in the cloud-edge scenarios faces the difficulty of resource collaboration and application collaboration management. To address these two issues, we propose a KubeEdge-cloud–edge-scheduling scheme named KCES. This workflow containerization scheduling scheme includes a cloud-edge workflow scheduling engine for KubeEdge and incorporates workflow scheduling strategies for tasks’ horizontal roaming and vertical offloading. This article proposes a cloud-edge workflow scheduling model and node model, as well as a workflow scheduling engine designed to maximize cloud-edge resource utilization under the constraint of workflow task delay. A cloud-edge resource hybrid management technology is used to devise the cloud-edge resource evaluation and resource allocation algorithms to achieve cloud-edge resource collaboration. Based on the ideas of distributed functional roles and the hierarchical division of computing power, the horizontal roaming among the edges and cloud-edge vertical offloading strategies for workflow tasks are designed to realize cloud-edge application collaboration. Experimental results using a customized IoT application workflow instance demonstrate that KCES outperforms three comparing algorithms in total workflow time, average workflow time, and resource usage and features horizontal roaming and vertical offloading of workflow tasks.

IEEE Internet of Things JournalVol. 12(2)
Beijing Institute of Technology (CN), Zaozhuang University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Shandong Province, Science and Technology Innovation Foundation of Harbin
Industry, innovation and infrastructure
Openalex Percentile: Top 2%
Cloud Computing and Resource Management
9.35
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