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Storage and computing resource enabled joint virtual resource allocation with QoS guarantee in mobile networks

  • Research Paper
  • Special Focus on Mobile Network Virtualization
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Abstract

Virtualization is the trend for the future mobile networks. With the advantage of virtualization, we can abstract the physical mobile network into the virtual network function (VNF) and design the network without the details. In this paper, we focus on the virtualization of the physical resources so that the resource allocation scheme considers not only the time-varying characteristic of wireless channels but also the amount of storage and computing resources. Virtual resources are composed of radio, storage and computing resources based on the virtualization technology. Since the cloud radio access network (C-RAN) is a successful paradigm to introduce computing resources into mobile networks, we investigate the virtual resource allocation scheme in the C-RAN architecture. With the content caching technology, we introduce the storage resources into joint resource allocation scheme further. In order to evaluate the performance of proposed scheme, we choose the effective capacity as the metric to include the influence of service latency. The purpose of the optimization problem is maximizing the system effective capacity with constraints of radio, storage and computing resources. It is simplified and converted into a convex problem solved by the subgradient method. Simulation results are provided to demonstrate performance gain of the effective capacity based joint resource allocation scheme.

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References

  1. Tesema F, Awada A, Viering I, et al. Evaluation of context-aware mobility robustness optimization and multiconnectivity in intra-frequency 5G ultra dense networks. IEEE Wirel Commun Lett, 2016, 5: 608–611

    Article  Google Scholar 

  2. Blasco P, Gündüz D. Learning-based optimization of cache content in a small cell base station. In: Proceedings of 2014 IEEE International Conference on Communications (ICC), Sydney, 2014. 1897–1903

    Chapter  Google Scholar 

  3. Peng M G, Wang C G, Li J, et al. Recent advances in underlay heterogeneous networks: interference control, resource allocation, and self-organization. IEEE Commun Surv Tut, 2015, 17: 700–729

    Article  Google Scholar 

  4. Xu D T, Ren P Y, Sun L, et al. Precoder-and-receiver design scheme for multi-user coordinated multi-point in LTE-A and fifth generation systems. IET Commun, 2016, 10: 292–299

    Article  Google Scholar 

  5. Liang C C, Yu F R. Wireless virtualization for next generation mobile cellular networks. IEEE Wirel Commun, 2015, 22: 61–69

    Article  Google Scholar 

  6. Peng M G, Li Y, Jiang J M, et al. Heterogeneous cloud radio access networks: a new perspective for enhancing spectral and energy efficiencies. IEEE Wirel Commun, 2014, 21: 126–135

    Article  Google Scholar 

  7. Sardellitti S, Barbarossa S, Scutari G. Distributed mobile cloud computing: joint optimization of radio and computational resources. In: Proceedings of 2014 IEEE Globecom Workshops (GC Wkshps), Austin, 2014. 1505–1510

    Chapter  Google Scholar 

  8. Cha M, Kwak H, Rodriguez P, et al. I tube, you tube, everybody tubes: analyzing the world’s largest user generated content video system. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, New York, 2007. 1–14

    Chapter  Google Scholar 

  9. Zhao Z Y, Jia S W, Li Y, et al. Performance analysis of cluster content caching in cloud-radio access networks. In: Proceedings of 2015 IEEE Globecom Workshops (GC Wkshps), San Diego, 2015. 1–6

    Google Scholar 

  10. Liao Y, Song L Y, Li Y H, et al. Radio resource management for cloud-RAN networks with computing capability constraints. In: Proceedings of 2016 IEEE International Conference on Communications, Kuala Lumpur, 2016. 1–6

    Google Scholar 

  11. Shanmugam K, Golrezaei N, Dimakis A G, et al. FemtoCaching: wireless content delivery through distributed caching helpers. IEEE Trans Inform Theory, 2013, 59: 8402–8413

    Article  MathSciNet  Google Scholar 

  12. Wu D P, Negi R. Effective capacity: a wireless link model for support of quality of service. IEEE Trans Wirel Commun, 2003, 2: 630–643

    Google Scholar 

  13. Wu D P, Negi R. Effective capacity-based quality of service measures for wireless networks. In: Proceedings of the 1st International Conference on Broadband Networks, San Jose, 2004. 527–536

    Google Scholar 

  14. Liu L J, Chamberland J F. On the effective capacities of multiple-antenna Gaussian channels. In: Proceedings of 2008 IEEE International Symposium on Information Theory, Toronto, 2008. 2583–2587

    Chapter  Google Scholar 

  15. Zhao Z Y, Peng M G, Ding Z G, et al. Cluster content caching: an energy-efficient approach to improve quality of service in cloud radio access networks. IEEE J Sel Areas Commun, 2016, 34: 1207–1221

    Article  Google Scholar 

  16. Boyd S, Vandenberghe L. Convex Optimization. Cambridge: Cambridge University Press, 2004. 1–50

    Google Scholar 

  17. Han X, Chen H F, Xie L, et al. Effective capacity region in a wireless multiuser OFDMA network. In: Proceedings of Global Communications Conference (GLOBECOM), Anaheim, 2012. 1794–1799

    Google Scholar 

  18. Boyd S, Mutapcic A. Subgradient Methods. Stanford: Stanford University Press, 2006. 1–35

    Google Scholar 

Download references

Acknowledgements

This work was supported by International Collaboration Project (Grant No. 2015DFT10-160), National Natural Science Foundation of China (Grant Nos. 61471068, 61421061, 61325006), National High-Tech R&D Program of China (863) (Grant No. 2014AA01A701), National Major Project (Grant No. 2016ZX03001009-003), Beijing Training Project for the Leading Talents in S&T (Grant No. Z141101001514026), and 111 Project of China (Grant No. B16006).

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Correspondence to Xiaodong Xu.

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Xu, X., Liu, J., Chen, W. et al. Storage and computing resource enabled joint virtual resource allocation with QoS guarantee in mobile networks. Sci. China Inf. Sci. 60, 040304 (2017). https://doi.org/10.1007/s11432-016-9038-7

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  • DOI: https://doi.org/10.1007/s11432-016-9038-7

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