Age of information-aware multi-tenant resource orchestration in network slicing

Xianfu Chen, Celimuge Wu, Tao Chen, Nan Wu, Honggang Zhang, Yusheng Ji

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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 languageEnglish
Title of host publicationProceedings - 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
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages1001-1007
Number of pages7
ISBN (Electronic)978-1-7281-3024-8
ISBN (Print)978-1-7281-3025-5
DOIs
Publication statusPublished - Aug 2019
MoE publication typeA4 Article in a conference publication
Event17th 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 20198 Aug 2019

Conference

Conference17th 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
CountryJapan
CityFukuoka
Period5/08/198/08/19

Keywords

  • Age of information
  • Deep reinforcement learning
  • Markov decision process
  • Network slicing
  • Stochastic game

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  • Cite this

    Chen, X., Wu, C., Chen, T., Wu, N., Zhang, H., & Ji, Y. (2019). Age of information-aware multi-tenant resource orchestration in network slicing. In 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 (pp. 1001-1007). [8890362] IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00182