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AFCC-r: Adaptive Feedback Congestion Control Algorithm to Avoid Queue Overflow in LTE Networks

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Abstract

With the dramatic increase in the number of users and the widespread use of smartphones, most of the internet content today is provided by cellular connections. The purpose of many active queue management algorithms developed for the cellular Long-Term Evolution network is to prevent forced packet drops in the Evolved Node B (eNodeB) Radio Link Control buffer and to improve delay and end-to-end throughput values. Although the algorithms developed in the literature improve some of the end-to-end throughput, delay, and packet data fraction values during bottleneck and congestion, they cannot balance these values. The proposed virtual queue management algorithm recalculates the average queue value and the packet dropping probability according to different traffic loads to solve the queue delay and queue overflow problem providing a balance between throughput, delay, and packet data fraction. Simulation results illustrate that the proposed algorithm reduces the delay of the packets and increases fairness among users compared to the Drop-tail, Random Early Drop, Controlled Delay, Proportional Integral Controller Enhanced, and Packet Limited First In First Out Queue algorithms.

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References

  1. Pacheco-Paramo D, Tello-Oquendo L (2020) Delay-aware dynamic access control for mMTC in wireless networks using deep reinforcement learning. Comput Netw. https://doi.org/10.1016/j.comnet.2020.107493

    Article  Google Scholar 

  2. Gao H, Liu C, Yin Y, Xu Y, Li Y (2021) A Hybrid Approach to Trust Node Assessment and Management for VANETs Cooperative Data Communication: Historical Interaction Perspective. IEEE Trans Intell Transp Syst 1–10. https://doi.org/10.1109/TITS.2021.3129458

  3. Paper W (2019) Cisco Visual Networking Index : Global Mobile Data Traffic Forecast, pp. 2017–2022

  4. A ND, R A (2019) Avoiding queue overflow and reducing queuing delay at eNodeB in LTE networks using congestion feedback mechanism. Comput Commun 146(May):131–143. https://doi.org/10.1016/j.comcom.2019.07.015

  5. Lai L, Feng D, Zheng F-C, Wang X, Yang HH, Quek TQS (2021) CQI-Based Interference Detection and Resource Allocation With QoS Provision in LTE-U Systems. IEEE Trans Veh Technol 70(2):1421–1433. https://doi.org/10.1109/TVT.2021.3052530

    Article  Google Scholar 

  6. Çakmak M, Albayrak Z, Torun C (2021) Performance Comparison of Queue Management Algorithms in LTE Networks using NS-3 Simulator. Tehnicki Vjesnik - Technical Gazette 28(1). https://doi.org/10.17559/TV-20200411071703

  7. Xiao J, Zheng J (2021) A Delay Balanced Adaptive Channel Allocation Mechanism for LTE-U and WiFi Coexistence Systems. Mob Netw Applic. https://doi.org/10.1007/s11036-020-01690-5

    Article  Google Scholar 

  8. Huang Y, Xu H, Gao H, Ma X, Hussain W (2021) SSUR: An Approach to Optimizing Virtual Machine Allocation Strategy Based on User Requirements for Cloud Data Center. IEEE Trans Green Commun Netw 5(2):670–681. https://doi.org/10.1109/TGCN.2021.3067374

    Article  Google Scholar 

  9. Ma L, Liu X, Wang H, Deng X (2020) Congestion tracking control for multi-router TCP/AQM network based on integral backstepping. Comput Netw 175:107278. https://doi.org/10.1016/j.comnet.2020.107278

    Article  Google Scholar 

  10. Singha S, Jana B, Mandal NK (2021) Active Queue Management in RED Considering Critical Point on Target Queue. J Interconnection Netw 21(03). https://doi.org/10.1142/S0219265921500171

  11. Gomez CA, Wang X, Shami A (2021) Federated Intelligence for Active Queue Management in Inter-Domain Congestion. IEEE Access 9:10674–10685. https://doi.org/10.1109/ACCESS.2021.3050174

    Article  Google Scholar 

  12. Wang H (2020) Trade-off queuing delay and link utilization for solving bufferbloat. ICT Express 6(4):269–272. https://doi.org/10.1016/j.icte.2020.05.008

    Article  Google Scholar 

  13. Yin Y, Huang Q, Gao H, Xu Y (2021) Personalized APIs Recommendation With Cognitive Knowledge Mining for Industrial Systems. IEEE Trans Industr Inf 17(9):6153–6161. https://doi.org/10.1109/TII.2020.3039500

    Article  Google Scholar 

  14. Çakmak M, Albayrak Z (2020) Performance Analysis of Queue Management Algorithms Between Remote-Host and PG-W in LTE Networks. Acad Platform J Eng Sci 456–463. https://doi.org/10.21541/apjes.662677

  15. Paul A, Kawakami H, Tachibana A, Hasegawa T (2017) Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network. Technologies 5(3):59. https://doi.org/10.3390/technologies5030059

    Article  Google Scholar 

  16. Wang YC, Hsieh SY (2016) Service-differentiated downlink flow scheduling to support QoS in long term evolution. Comput Netw 94(2016):344–359. https://doi.org/10.1016/j.comnet.2015.11.002

    Article  Google Scholar 

  17. Hanczewski S, Stasiak M, Weissenberg J (2018) Queueing model of a multi-service system with elastic and adaptive traffic. Comput Netw 147:146–161. https://doi.org/10.1016/j.comnet.2018.09.023

    Article  Google Scholar 

  18. Pan R, et al. (2013) PIE: A lightweight control scheme to address the bufferbloat problem. IEEE International Conference on High Performance Switching and Routing, HPSR, pp. 148–155. https://doi.org/10.1109/HPSR.2013.6602305

  19. Nichols K, Jacobson V (2012) Controlling queue delay. Commun ACM 55(7):42–50. https://doi.org/10.1145/2209249.2209264

    Article  Google Scholar 

  20. Jiang H, Wang Y, Lee K, Rhee I (2016) DRWA: A receiver-centric solution to bufferbloat in cellular networks. IEEE Trans Mob Comput 15(11):2719–2734. https://doi.org/10.1109/TMC.2015.2510641

    Article  Google Scholar 

  21. Liu Y, Jing Y, Chen X (2019) Adaptive neural practically finite-time congestion control for TCP/AQM network. Neurocomputing 351:26–32. https://doi.org/10.1016/j.neucom.2019.03.022

    Article  Google Scholar 

  22. Feng C-W, Huang L-F, Xu C, Chang Y-C (2017) Congestion Control Scheme Performance Analysis Based on Nonlinear RED. IEEE Syst J 11(4):2247–2254. https://doi.org/10.1109/JSYST.2014.2375314

    Article  Google Scholar 

  23. Gao H, Qin X, Barroso RJD, Hussain W, Xu Y, Yin Y (2020) Collaborative Learning-Based Industrial IoT API Recommendation for Software-Defined Devices: The Implicit Knowledge Discovery Perspective. IEEE Trans Emerg Top Comput Intell 1–11. https://doi.org/10.1109/TETCI.2020.3023155

  24. Zheng W, Li Y, Jing X, Liu S (2020) Adaptive Finite-Time Congestion Control for Uncertain TCP/AQM Network with Unknown Hysteresis. Complexity 2020:1–9. https://doi.org/10.1155/2020/4138390

    Article  MATH  Google Scholar 

  25. Brakmo LS, Peterson LL (1995) TCP Vegas: End to End Congestion Avoidance on a Global Internet. IEEE J Sel Areas Commun 13(8):1465–1480. https://doi.org/10.1109/49.464716

    Article  Google Scholar 

  26. Im H, Joo C, Lee T, Bahk S (2016) Receiver-Side TCP Countermeasure to Bufferbloat in Wireless Access Networks. IEEE Trans Mob Comput 15(8):2080–2093. https://doi.org/10.1109/TMC.2015.2483494

    Article  Google Scholar 

  27. Belamfedel Alaoui S, Tissir EH, Chaibi N (2020) Analysis and design of robust guaranteed cost Active Queue Management. Comput Commun 159:124–132. https://doi.org/10.1016/j.comcom.2020.05.009

  28. Dai T, Zhang X, Zhang Y, Guo Z (2020) Statistical Learning Based Congestion Control for Real-Time Video Communication. IEEE Trans Multimed 22(10):2672–2683. https://doi.org/10.1109/TMM.2019.2959448

    Article  Google Scholar 

  29. Li J, Yuan Y, Ruan T, Chen J, Luo X (2021) A proportional-integral-derivative-incorporated stochastic gradient descent-based latent factor analysis model. Neurocomputing 427:29–39. https://doi.org/10.1016/j.neucom.2020.11.029

    Article  Google Scholar 

  30. Singh PK, Gupta SK (2011) Variable length virtual output queue based fuzzy congestion control at routers. 2011 IEEE 3rd International Conference on Communication Software and Networks, ICCSN 2011, pp. 29–33. https://doi.org/10.1109/ICCSN.2011.6013654

  31. Senapati R (2021) LTE-advanced cell capacity estimation model and algorithm for voice service. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03373-9

    Article  Google Scholar 

  32. Zhang D, Zhang T, Liu X (2019) Novel self-adaptive routing service algorithm for application in VANET. Appl Intell. https://doi.org/10.1007/s10489-018-1368-y

    Article  Google Scholar 

  33. gan Zhang D, Zhang T, Dong Y, huan Liu X, ya Cui Y, xin Zhao D (2018) Novel optimized link state routing protocol based on quantum genetic strategy for mobile learning. J Netw Comput Applic. https://doi.org/10.1016/j.jnca.2018.07.018

  34. Biswas S, Gupta A, Chakraborty S (2021) Load-balanced user associations in dense LTE networks. Comput Netw 189:107928. https://doi.org/10.1016/j.comnet.2021.107928

    Article  Google Scholar 

  35. Liu S et al (2019) Dynamic Analysis for the Average Shortest Path Length of Mobile Ad Hoc Networks Under Random Failure Scenarios. IEEE Access 7:21343–21358. https://doi.org/10.1109/ACCESS.2019.2896699

    Article  Google Scholar 

  36. Beshay JD, Nasrabadi AT, Prakash R, Francini A (2017) On Active Queue Management in Cellular Networks, pp. 384–389. https://doi.org/10.1109/INFCOMW.2017.8116407

  37. Vijayakumar A, Selvamani K, Pradeep A (2015) Reputed Packet Delivery using Efficient Audit Misbehaviour Detection and Monitoring Method in Mobile Ad Hoc Networks. Procedia - Procedia Computer Science 48(Iccc):489–496. https://doi.org/10.1016/j.procs.2015.04.124

  38. Ramdev MS, Bajaj R, Sidhu J (2021) Remote Radio Head Scheduling in LTE-Advanced Networks. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08916-z

    Article  Google Scholar 

  39. Wang D, Wang P, Wang C (2020) Efficient Multi-Factor User Authentication Protocol with Forward Secrecy for Real-Time Data Access in WSNs. ACM Trans Cyber-Phys Syst 4(3):1–26. https://doi.org/10.1145/3325130

    Article  Google Scholar 

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Correspondence to Muhammet Çakmak.

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Çakmak, M., Albayrak, Z. AFCC-r: Adaptive Feedback Congestion Control Algorithm to Avoid Queue Overflow in LTE Networks. Mobile Netw Appl 27, 2138–2152 (2022). https://doi.org/10.1007/s11036-022-02011-8

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