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Wireless Networks

, Volume 25, Issue 8, pp 5173–5185 | Cite as

A novel scheduling algorithm to improve SUPT for multi-queue multi-server system

  • Yake Li
  • Xinpeng FangEmail author
  • Weisheng Chen
Article
  • 27 Downloads

Abstract

This study improves the Quality of Experience (QoE) of the multi-queue multi-server queueing system by solving the scheduling problem. The QoE is evaluated by a novel indicator named system user-perceived throughput (SUPT). According to the property of the traffic, the stochastic optimization problem for SUPT can be transformed into utility maximization under the constraint of queue stability. We then propose a drift-plus-penalty scheduling algorithm named max modified weight (MMW) to balance delay and utility. A Nike function for queue length replaces the queue length as the weight. Furthermore, we prove the stability of the queues based on the Foster–Lyapunov theorem and analyze the delay boundary under the proposed MMW scheduling algorithm. Finally, compared with several classical scheduling policies, the effectiveness of the MMW is verified by evaluating the average system throughput, SUPT, the average system backlog, and user-perceived throughput of the queues in three different scenarios. The simulation results show MMW policy achieves more efficient trade-off between SUPT and system delay, and is capable of maintaining system stability as max weight regardless of the system load.

Keywords

Quality of Experience Queue stability System user perceived throughput Scheduling algorithm Multi-queue multi-server system 

Abbreviations

3GPP

3rd generation partnership project

LTE

Long term evolution

QoE

Quality of Experience

UPT

User-perceived throughput

SUPT

System user-perceived throughput

OFDM

Orthogonal frequency division multiplexing

CSI

Channel state information

QSI

Queue state information

MDP

Markov decision process

PP

Poisson process

IPP

Interrupted Poisson process

MQMS

Multi-queue multi-server

RLC

Radio link control

MAC

Media access control

HTTP

Hyper text transfer protocol

SIPT

Scheduled internet protocol throughput

Notes

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 61703326 and 61673308, in part by the Fundamental Research Funds for the Central Universities under Grant JB181307, and in part by the Innovation Fund of Xidian University.

References

  1. 1.
    3GPP TS 36.314. (2018). Evolved Universal Terrestrial Radio Access (E-UTRA); Layer 2—Measurements (Release 15). Technical specification group radio access network.Google Scholar
  2. 2.
    Andrews, M., & Zhang, L. (2011). Scheduling algorithms for multicarrier wireless data systems. IEEE/ACM Transactions on Networking, 19(2), 447–455.CrossRefGoogle Scholar
  3. 3.
    Yang, H., Ren, F., Lin, C., & Zhang, J. (2010). Frequency-domain packet scheduling for 3GPP LTE uplink. In IEEE INFOCOM, pp. 1–9.Google Scholar
  4. 4.
    Jun, D., Jiang, C., Wang Jian, Y., Shui, H. Z., & Yong, R. (2017). Resource allocation in space multi-access systems. IEEE Transactions on Aerospace and Electronic Systems, 53(2), 598–618.CrossRefGoogle Scholar
  5. 5.
    Chaudhuri, S., Baig, I., & Das, D. (2015). Utility based QoS aware uplink scheduler scheme for LTE small cell network. In IEEE international conference on communications, pp. 3149–3154.Google Scholar
  6. 6.
    Dechene Dan, J., & Shami, A. (2013). Energy efficient QoS constrained scheduler for SC-FDMA uplink. Physical Communication, 8(8), 81–90.CrossRefGoogle Scholar
  7. 7.
    Neely, M. J. (2006). Energy optimal control for time-varying wireless networks. IEEE Transactions on Information Theory, 52(7), 2915–2934.MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Manikandan, C., Bhashyam, S., & Sundaresan, R. (2009). Cross-layer scheduling with infrequent channel and queue measurements. IEEE Transactions on Wireless Communications, 8(12), 5737–5742.CrossRefGoogle Scholar
  9. 9.
    Kittipiyakul, S., & Javidi, T. (2009). Delay-optimal server allocation in multiqueue multiserver systems with time-varying connectivities. IEEE Transactions on Information Theory, 55(5), 2319–2333.MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Halabian, H., Lambadaris, I., & Lung, C. H. (2014). Explicit characterization of stability region for stationary multi-queue multi-server systems. IEEE Transactions on Automatic Control, 59(2), 355–370.MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Capozzi, F., Piro, G., Grieco, L. A., Boggia, G., & Camarda, P. (2013). Downlink packet scheduling in LTE cellular networks: Key design issues and a survey. IEEE Communications Surveys Tutorials, 15(2), 678–700.CrossRefGoogle Scholar
  12. 12.
    Andrews, M. (2004). Instability of the proportional fair scheduling algorithm for HDR. IEEE Transactions on Wireless Communications, 3(5), 1422–1426.CrossRefGoogle Scholar
  13. 13.
    Tassiulas, L., & Ephremides, A. (1992). Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 37(12), 1936–1948.MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Marques, A. G., Lopez-Ramos, L. M., Giannakis, G. B., Ramos, J., & Caamano, A. J. (2012). Optimal cross-layer resource allocation in cellular networks using channel and queue-state information. IEEE Transactions on Vehicular Technology, 61(6), 2789–2807.CrossRefGoogle Scholar
  15. 15.
    Chen, J., & Lau, V. K. N. (2013). Delay analysis of max-weight queue algorithm for time-varying wirelessadhoc networks—Control theoretical approach. IEEE Transactions on Signal Processing, 61(1), 99–108.MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Neely, M. J., & Supittayapornpong, S. (2013). Dynamic Markov decision policies for delay constrained wireless scheduling. IEEE Transactions on Automatic Control, 58(8), 1948–1961.MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Neely, M. J. (2009). Delay analysis for maximal scheduling with flow control in wireless networks with bursty traffic. IEEE Transactions on Networking, 17(4), 1146–1159.CrossRefGoogle Scholar
  18. 18.
    Neely, M. (2010). Stochastic network optimization with application to communication and queueing systems. Synthesis Lectures on Communication Networks, 3(1), 211.zbMATHCrossRefGoogle Scholar
  19. 19.
    Zhou, Y., Kumar, R., & Tang, S. (2018). Incentive-based distributed scheduling of electric vehicle charging under uncertainty. IEEE Transactions on Power Systems, 34(1), 3–11.CrossRefGoogle Scholar
  20. 20.
    Shi, W., Li, N., Chu, C.-C., & Gadh, R. (2017). Real-time energy management in microgrids. IEEE Transactions on Smart Grid, 8(1), 228–238.CrossRefGoogle Scholar
  21. 21.
    Li, Y., Sheng, M., Wang, C.-X., Wang, X., Shi, Y., & Li, J. (2015). Throughput-delay tradeoff in interference-free wireless networks with guaranteed energy efficiency. IEEE Transactions on Wireless Communications, 14(3), 1608–1621.CrossRefGoogle Scholar
  22. 22.
    Peng, M., Yu, Y., Xiang, H., & Poor, H. V. (2016). Energy-efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks. IEEE Transactions on Multimedia, 18(5), 879–892.CrossRefGoogle Scholar
  23. 23.
    Jun, D., Jiang, C., Yi, Q., Zhu, H., & Yong, R. (2016). Resource allocation with video traffic prediction in cloud-based space systems. IEEE Transactions on Multimedia, 18(5), 1–1.CrossRefGoogle Scholar
  24. 24.
    Brueck, S., Zhao, L., Giese, J., & Amin, M. A. (2010). Centralized scheduling for joint transmission coordinated multi-point in LTE-Advanced. In 2010 International ITG workshop on smart antennas (WSA). IEEE, pp. 177–184.Google Scholar
  25. 25.
    Ishiguro, A. G. (2014). Scheduling and resource allocation for mobile broadband networks. PhD thesis, The University of Texas at Austin.Google Scholar
  26. 26.
    Eryilmaz, A., & Srikant, R. (2012). Asymptotically tight steady-state queue length bounds implied by drift conditions. Queueing Systems, 72(3–4), 311–359.MathSciNetzbMATHCrossRefGoogle Scholar
  27. 27.
    Seo, J.-B., & Jin, H. (2017). Stability region of p-persistent csma systems. IEEE Communications Letters, 21(3), 652–655.CrossRefGoogle Scholar
  28. 28.
    Jian, W., Bao, Y., Miao, G., Zhou, S., & Niu, Z. (2016). Base-station sleeping control and power matching for energy-delay tradeoffs with bursty traffic. IEEE Transactions on Vehicular Technology, 65(5), 3657–3675.CrossRefGoogle Scholar
  29. 29.
    Georgiadis, L., Neely, M. J., & Tassiulas, L. (2006). Resource allocation and cross-layer control in wireless networks. Foundations Trends in Networking, 1(1), 1–144.zbMATHCrossRefGoogle Scholar
  30. 30.
    Neely, M. J., Modiano, E., & Li, C. P. (2008). Fairness and optimal stochastic control for heterogeneous networks. IEEE/ACM Transactions on Networking, 16(2), 396–409.CrossRefGoogle Scholar
  31. 31.
    Ross, S. M. (2014). Introduction to probability models. Cambridge: Academic Press.zbMATHGoogle Scholar
  32. 32.
    Al-Dweik, A., Kalil, M., & Shami, A. (2015). Qos-aware power-efficient scheduler for LTE uplink. IEEE Transactions on Mobile Computing, 14(8), 1672–1685.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Aerospace Science and TechnologyXidian UniversityXi’anChina

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