The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Migration Modeling and Learning Algorithms for Containers in Fog Computing
Abstract
Fog Computing (FC) is a flexible architecture to support distributed domain-specific applications with cloud-like quality of service. However, current FC still lacks the mobility support mechanism when facing many mobile users with diversified application quality requirements. Such mobility support mechanism can be critical such as in the industrial internet where human, products, and devices are moveable. To fill in such gaps, in this paper we propose novel container migration algorithms and architecture to support mobility tasks with various application requirements. Our algorithms are realized from three aspects: 1) We consider mobile application tasks can be hosted in a container of a corresponding fog node that can be migrated, taking the communication delay and computational power consumption into consideration; 2) We further model such container migration strategy as multiple dimensional Markov Decision Process (MDP) spaces. To effectively reduce the large MDP spaces, efficient deep reinforcement learning algorithms are devised to achieve fast decision-making and 3) We implement the model and algorithms as a container migration prototype system and test its feasibility and performance. Extensive experiments show that our strategy outperforms the existing baseline approaches 2.9, 48.5 and 58.4 percent on average in terms of delay, power consumption, and migration cost, respectively.
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Cites
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Fog computing and its role in the internet of things
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“In addition, there are some concepts similar to FC such as mobile computing [46], mobile cloud computing [3], edge computing [47], etc.”
BackgroundResource Management with Deep Reinforcement Learning
Mao, H., Alizadeh, M., Menache, I., & Kandula, S. (2016) · Proceedings of the 15th ACM Workshop on Hot Topics in Networks · doi:10.1145/3005745.3005750 · Cited by 1,353 · cited 1×
“[58] use the deep reinforcement learning in network resource scheduling.”
MethodEdge-centric Computing: Vision and Challenges
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Cited by
Mobile-kube: mobility-aware and energy-efficient service orchestration on kubernetes edge servers
Ghafouri, S., Karami, A., Bakhtiarvan, D. B., Bigdeli, A. S., Gill, S. S., & Doyle, J. (2022) · 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) · doi:10.1109/ucc56403.2022.00019 · Cited by 20
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Luong, N. C., Hoang, D. T., Gong, S., Niyato, D., Wang, P., Liang, Y.-C., & Kim, D. I. (2018) · arXiv · doi:10.48550/arxiv.1810.07862 · Cited by 17
Dynamic shift from cloud computing to industry 4.0: eco-friendly choice or climate change threat
Singh, M., Tuli, S., Butcher, R. J., Kaur, R., & Gill, S. S. (2021) · Lecture Notes on Data Engineering and Communications Technologies · doi:10.1007/978-3-030-71172-6_12 · Cited by 6
Metadata
- Authors
- Tang, Z., Zhou, X., Zhang, F., Jia, W., & Zhao, W.
- Venue
- IEEE Transactions on Services Computing
- Published
- 2019-09-01
- Volume
- 12
- Issue
- 5
- Pages
- 712-725
- ISSN
- 1939-1374 (ISSN-L)
- 2372-0204
- OpenAlex
- W2801779586
- Cited by
- 229
- References
- 11
- FWCI
- 15.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- Tang, Z., Zhou, X., Zhang, F., Jia, W., & Zhao, W. (2019). Migration Modeling and Learning Algorithms for Containers in Fog Computing. IEEE Transactions on Services Computing. https://doi.org/10.1109/tsc.2018.2827070
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