Linear Random Early Detection for Congestion Control at the Router Buffer
Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the performance at the router specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method.
Abu-Shareha, A.A., Enhanced Random Early Detection using Responsive Congestion Indicators. International Journal of Advanced Computer Science and Applications(IJACSA), 2019. 10(3): p. 358-367.
Baklizi, M., et al., Fuzzy logic controller of gentle random early detection based on average queue length and delay rate. International Journal of Fuzzy Systems, 2014. 16(1): p. 9-19.
Mohammed, H., G. Attiya, and S. El-Dolil, Active Queue Management for Congestion Control: Performance Evaluation, New Approach, and Comparative Study. International Journal of Computing and Network Technology, 2017. 5(02): p. 37-49.
Abualhaj, M.M., A.A. Abu-Shareha, and M.M. Al-Tahrawi, FLRED: an efficient fuzzy logic based network congestion control method. Neural Computing and Applications, 2018. 30(3): p. 925–935.
Floyd, S. Recommendations On Using the Gentle Variant of RED. http://www.aciri.org/floyd/red/gentle.html 2000.
Seifaddini, O., A. Abdullah, and a.H. Vosough, RED, GRED, AGRED CONGESTION CONTROL ALGORITHMS IN HETEROGENEOUS TRAFFIC TYPES, in International Conference on Computing and Informatics. 2013.
Abu-Shareha, A.A., Controlling Delay at the Router Buffer Using Modified Random Early Detection. International Journal of Computer Networks & Communications (IJCNC), 2019. 11(6): p. 63-75.
Floyd, S. and V. Jacobson, Random early detection gateways for congestion avoidance. IEEE/ACM Trans. Netw., 1993. 1(4): p. 397-413.
Chhabra, K., M. Kshirsagar, and A. Zadgaonkar, An Improved RED Algorithm with Input Sensitivity, in Cyber Security. 2018, Springer. p. 35-45.
Sharma, N., et al., P-RED: Probability Based Random Early Detection Algorithm for Queue Management in MANET, in Advances in Computer and Computational Sciences. 2018, Springer. p. 637-643.
Feng, W.-c., et al., BLUE: A New Class of Active Queue Management Algorithms. 1999, University of Michigan, Ann Arbor, MI, Technical Report.
Koo, J., S. Ahn, and J. Chung, A comparative study of queue, delay, and loss characteristics of AQM schemes in QoS-enabled networks. Computing and Informatics, 2004. 23(4): p. 317-335.
Liu, S., T. Başar, and R. Srikant, TCP-Illinois: A loss-and delay-based congestion control algorithm for high-speed networks. Performance Evaluation, 2008. 65(6): p. 417-440.
Lim, L.B., et al., Controlling mean queuing delay under multi-class bursty and correlated traffic. Journal of Computer and System Sciences, 2011. 77(5): p. 898-916.
Wang, P., et al. Active queue management of delay network based on constrained model predictive control. in 2011 Chinese Control and Decision Conference (CCDC). 2011. IEEE.
Feng, W.-c., et al., The BLUE active queue management algorithms. Networking, IEEE/ACM Transactions on, 2002. 10(4): p. 513-528.
Li, J.-S. and Y.-S. Su, Random early detection with flow number estimation and queue length feedback control. Journal of Systems Architecture, 2006. 52(6): p. 359-372.
Stanojevic, R., R.N. Shorten, and C.M. Kellett, Adaptive tuning of drop-tail buffers for reducing queueing delays. IEEE Communications Letters, 2006. 10(7): p. 570-572.
Hadjadj-Aoul, Y., Towards AQM Cooperation for Congestion Avoidance in DiffServ/MPLS Networks. Recent Patents on Computer Science, 2009. 2(1): p. 1-13.
AL-DIABAT, M., et al., Analytical models based discrete-time queueing for the congested network. International Journal of Modeling, Simulation, and Scientific Computing, 2012. 3(01): p. 1150004.
Patel, S. and S. Bhatnagar, Adaptive Mean Queue Size and Its Rate of Change: Queue Management with Random Dropping. arXiv preprint arXiv:1602.02241, 2016.
Tsavlidis, L., P. Efraimidis, and R.-A. Koutsiamanis, Prince: an effective router mechanism for networks with selfish flows. Journal of Internet Engineering, 2016. 6(1).
Ott, T.J., T.V. Lakshman, and L. Wong. SRED: stabilized RED. in INFOCOM '99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. 1999.
Moghaddam, M.H.Y., A fuzzy Active Queue Management mechanism for Internet congestion control, in Third International Workshop on Advanced Computational Intelligence (IWACI). 2010, IEEE. p. 203-208.
Mohammadi, S., et al., Fuzzy-based PID active queue manager for TCP/IP networks, in International Conference on Information Sciences Signal Processing and their Applications (ISSPA). 2010, IEEE. p. 434-439.
Ingoley, S.N. and M. Nashipudi, A Review: Fuzzy Logic in Congestion Control of Computer Network in International Conference in Recent Trends in Information Technology and Computer Science 2012.
Nassar, K.A. and A.A. Abdullah, Fuzzy RED to Reduce Packet Loss in Computer network. Journal of AL-Qadisiyah for computer science and mathematics, 2016. 8(1): p. 107-114.
Khatari, M. and G. Samara, Congestion control approach based on effective random early detection and fuzzy logic. arXiv preprint arXiv:1712.04247, 2017.
Kunniyur, S. and R. Srikant, End-to-end congestion control schemes: Utility functions, random losses and ECN marks. Networking, IEEE/ACM Transactions on, 2003. 11(5): p. 689-702.
Long, C.-N., B. Zhao, and X.-P. Guan, SAVQ: Stabilized adaptive virtual queue management algorithm. Communications Letters, IEEE, 2005. 9(1): p. 78-80.
Yanping, Q., et al. A stable enhanced adaptive virtual queue management algorithm for TCP networks. in Control and Automation, 2007. ICCA 2007. IEEE International Conference on. 2007. IEEE.
Abbasov, B. and S. Korukoglu, Effective RED: An algorithm to improve RED's performance by reducing packet loss rate. Journal of Network and Computer Applications, 2009. 32(3): p. 703-709.
Zhang, J., W. Xu, and L. Wang, An Improved Adaptive Active Queue Management Algorithm Based on Nonlinear Smoothing. Procedia Engineering, 2011. 15: p. 2369-2373.
Chrysostomou, C., et al., Fuzzy Explicit Marking for Congestion Control in Differentiated Services Networks, in Proceedings of the Eighth IEEE International Symposium on Computers and Communications. 2003, IEEE Computer Society.
Yaghwaei, M., M.B. Menhaj, and H. Amintoosi, A fuzzy extension to the blue active queue management algorithm. 2004.
Zargar, S.T., M.H. Yaghmaee, and A.M. Fard, Fuzzy proactive queue management technique, in Annual IEEE India Conference. 2006, IEEE. p. 1-6.
Abdel-Jaber, H., et al. Fuzzy logic controller of Random Early Detection based on average queue length and packet loss rate. in Performance Evaluation of Computer and Telecommunication Systems, 2008. SPECTS 2008. International Symposium on. 2008.
Lapsley, D. and S. Low. Random early marking: an optimisation approach to Internet congestion control. in Networks, 1999. (ICON '99) Proceedings. IEEE International Conference on. 1999.
Koo, J., et al. MRED: a new approach to random early detection. in Information Networking, 2001. Proceedings. 15th International Conference on. 2001. IEEE.
Yu-hong, Z., Z. Xue-feng, and T. Xu-yan, Research on the Improved Way of RED Algorithm S-RED. International Journal of u-and e-Service, Science and Technology, 2016. 9(2): p. 375-384.
Zhao, Y., et al., An Improved Algorithm of Nonlinear RED Based on Membership Cloud Theory. Chinese Journal of Electronics, 2017. 26(3): p. 537-543.
Patel, Z.M. Queue occupancy estimation technique for adaptive threshold based RED. in Circuits and Systems (ICCS), 2017 IEEE International Conference on. 2017. IEEE.
Bhatnagar, S. and S. Patel, A stochastic approximation approach to active queue management. Telecommunication Systems, 2018. 68(1): p. 89-104.
Floyd, S., R. Gummadi, and S. Shenker, Adaptive RED: An Algorithm for Increasing the Robustness of RED's Active Queue Management. AT&T Center for Internet Research at ICSI, 2001.
Aweya, J., M. Ouellette, and D.Y. Montuno, A control theoretic approach to active queue management. Comput. Netw., 2001. 36(2-3): p. 203-235.
Hollot, C.V., et al. On designing improved controllers for AQM routers supporting TCP flows. in INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE. 2001. IEEE.
Masoumzadeh, S.S., et al., Deep Blue: A Fuzzy Q-Learning Enhanced Active Queue Management Scheme, in International Conference on Adaptive and Intelligent Systems (ICAIS'09). 2009, IEEE. p. 43-48.
Lin, D. and R. Morris. Dynamics of random early detection. in ACM SIGCOMM Computer Communication Review. 1997. ACM.
Agarwal, M., R. Gupta, and V. Kargaonkar. Link utilization based AQM and its performance. in Global Telecommunications Conference, 2004. GLOBECOM'04. IEEE. 2004. IEEE.
This work is licensed under a Creative Commons Attribution 3.0 License.