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Abstract
To satisfy diverse services from mobile users (MUs) over a common network infrastructure, network slicing is envisioned as a promising technology. This paper considers radio access network (RAN)-only slicing, where the physical RAN is judiciously tailored to accommodate computation and communication functionalities. Multiple service providers (SPs, a.k.a., tenants) compete for a limited number of channels across the discrete scheduling slots in order to serve their respective subscribed MUs. From a MU perspective, the age of information of data packets from traditional mobile services and the energy consumption at mobile device are of practical importance. We characterize the interactions among the SPs via a stochastic game, in which a SP selfishly maximizes its own expected long-term payoff. To approximate the Nash equilibrium solutions, we build an abstract stochastic game exploring the local information of SPs. Furthermore, the decision-making process at a SP can be much simplified by linearly decomposing the per-SP Markov decision process, for which we derive a deep reinforcement learning based scheme to find the optimal abstract control policies. TensorFlow-based experiments validate our studies and show that the proposed scheme outperforms the three baselines and yields the best performance in average utility.
Original language | English |
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Title of host publication | Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 1001-1007 |
Number of pages | 7 |
ISBN (Electronic) | 978-1-7281-3024-8 |
ISBN (Print) | 978-1-7281-3025-5 |
DOIs | |
Publication status | Published - Aug 2019 |
MoE publication type | A4 Article in a conference publication |
Event | 17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 - Fukuoka, Japan Duration: 5 Aug 2019 → 8 Aug 2019 |
Conference
Conference | 17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019 |
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Country/Territory | Japan |
City | Fukuoka |
Period | 5/08/19 → 8/08/19 |
Funding
The work carried out in this paper was supported by the Academy of Finland under Grant 319759, the JSPS KAKENHI under Grant 18KK0279, the JST-Mirai Program under Grant JPMJMI17B3, the Telecommunications Advanced Foundation, the National Key R&D Program of China under Grant 2017YFB1301003, the National Natural Science Foundation of China under Grants 61701439 and 61731002, and the Zhejiang Key Research and Development Plan under Grant 2019C01002.
Keywords
- Age of information
- Deep reinforcement learning
- Markov decision process
- Network slicing
- Stochastic game
Fingerprint
Dive into the research topics of 'Age of information-aware multi-tenant resource orchestration in network slicing'. Together they form a unique fingerprint.Projects
- 1 Finished
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MISSION: Mission-Critical Internet of Things Applications over Fog Networks
Chen, X. (CoPI), Forsell, M. (Participant), Chen, T. (Participant) & Räty, T. (Participant)
1/01/19 → 31/12/21
Project: Academy of Finland project
Prizes
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Best Paper Award at DASC-PiCom-CBDCom-CyberSciTech 2019 conference
Chen, X. (Recipient), Wu, C. (Recipient), Chen, T. (Recipient), Wu, N. (Recipient), Zhang, H. (Recipient) & Ji, Y. (Recipient), 2019
Prize: Prize for a work