A Resource Management Scheme Based on Live Migrations for Mobility Support in Edge-Based Fog Computing Environments


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 4, pp. 163-168, Apr. 2022
https://doi.org/10.3745/KTSDE.2022.11.4.163,   PDF Download:
Keywords: Cloud-fog Computing, resource management, Internet of Things, Edge Computing
Abstract

As cloud computing and the Internet of things are getting popular, the number of devices in the Internet of things computing environments is increasing. In addition, there exist various Internet-based applications, such as home automation and healthcare. In turn, existing studies explored the quality of service, such as downtime and reliability of tasks for Internet of things applications. To enhance the quality of service of Internet of things applications, cloud-fog computing (combining cloud computing and edge computing) can be used for offloading burdens from the central cloud server to edge servers. However, when devices inherit the mobility property, continuity and the quality of service of Internet of things applications can be reduced. In this paper, we propose a resource management scheme based on live migrations for mobility support in edge-based fog computing environments. The proposed resource management algorithm is based on the mobility direction and pace to predict the expected position, and migrates tasks to the target edge server. The performance results show that our proposed resource management algorithm improves the reliability of tasks and reduces downtime of services.


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Cite this article
[IEEE Style]
J. Lim, "A Resource Management Scheme Based on Live Migrations for Mobility Support in Edge-Based Fog Computing Environments," KIPS Transactions on Software and Data Engineering, vol. 11, no. 4, pp. 163-168, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.4.163.

[ACM Style]
JongBeom Lim. 2022. A Resource Management Scheme Based on Live Migrations for Mobility Support in Edge-Based Fog Computing Environments. KIPS Transactions on Software and Data Engineering, 11, 4, (2022), 163-168. DOI: https://doi.org/10.3745/KTSDE.2022.11.4.163.