Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jan 30;23(3):1522.
doi: 10.3390/s23031522.

Local Scheduling in KubeEdge-Based Edge Computing Environment

Seong-Hyun Kim  1 , Taehong Kim  1
Affiliations

Local Scheduling in KubeEdge-Based Edge Computing Environment

Seong-Hyun Kim et al. Sensors (Basel). .

Abstract

KubeEdge is an open-source platform that orchestrates containerized Internet of Things (IoT) application services in IoT edge computing environments. Based on Kubernetes, it supports heterogeneous IoT device protocols on edge nodes and provides various functions necessary to build edge computing infrastructure, such as network management between cloud and edge nodes. However, the resulting cloud-based systems are subject to several limitations. In this study, we evaluated the performance of KubeEdge in terms of the computational resource distribution and delay between edge nodes. We found that forwarding traffic between edge nodes degrades the throughput of clusters and causes service delay in edge computing environments. Based on these results, we proposed a local scheduling scheme that handles user traffic locally at each edge node. The performance evaluation results revealed that local scheduling outperforms the existing load-balancing algorithm in the edge computing environment.

Keywords: EdgeMesh; KubeEdge; Kubernetes; edge computing; load balancer; microservice.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
KubeEdge architecture and its components in cloud and edge.
Figure 2
Figure 2
Load-balancing schemes in KubeEdge.
Figure 3
Figure 3
Local scheduling scheme in KubeEdge.
Figure 4
Figure 4
Experimental setup.
Figure 5
Figure 5
Performance at Edge node 1 with different numbers of pods: (a) throughput and (b) average response time.
Figure 6
Figure 6
Throughput and average response time according to pod distribution and delay between edge nodes (a,d): 4-4-4, (b,e): 8-3-1, (c,f): 10-1-1.
Figure 7
Figure 7
Throughput and average response time according to the load-balancing schemes (a,c): round-robin scheme, (b,d): local scheduling scheme.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Pan J., McElhannon J. Future Edge Cloud and Edge Computing for Internet of Things Applications. IEEE Internet Things J. 2017;5:439–449. doi: 10.1109/JIOT.2017.2767608. - DOI
    1. Sadri A.A., Rahmani A.M., Saberikamarposhti M., Hosseinzadeh M. Fog data management: A vision, challenges, and future directions. J. Netw. Comput. Appl. 2021;174:102882. doi: 10.1016/j.jnca.2020.102882. - DOI
    1. Zhou N., Georgiou Y., Pospieszny M., Zhong L., Zhou H., Niethammer C., Pejak B., Marko O., Hoppe D. Container orchestration on HPC systems through Kubernetes. J. Cloud Comput. 2021;10:16. doi: 10.1186/s13677-021-00231-z. - DOI
    1. Pahl C. Containerization and the PaaS Cloud. J. Cloud Comput. 2015;2:24–31. doi: 10.1109/MCC.2015.51. - DOI
    1. Nguyen N.D., Phan L.A., Park D.H., Kim S., Kim T. ElasticFog: Elastic Resource Provisioning in Container-Based Fog Computing. IEEE Access. 2020;8:183879–183890. doi: 10.1109/ACCESS.2020.3029583. - DOI

LinkOut - more resources