Chronology of the development of active queue management algorithms of RED family. Part 3: from 2016 up to 2024

Cover Page

Cite item

Full Text

Abstract

This work is the first part of a large bibliographic review of active queue management algorithms of the Random Early Detection (RED) family, presented in the scientific press from 1993 to 2023. The third part will provide data on algorithms published from 2016 to 2023.

Full Text

1. Introduction This work is the third (the last) part of the brief bibliographic review of algorithms of the Random Early Detection (RED) family, compiled according to the dates of publication of scientific works (articles and conference proceedings) in which the algorithms in question were presented to the public. The previous parts were presented in [1, 2] The authors do not claim that the prepared review includes all existing algorithms, but is the most complete of those published previously, since it includes bibliographic data on more then 240 algorithms. The characteristics of the RED algorithm, as the reasons for its modifications were presented and described in [1, 2]. The review is structured as follows. Each subsequent section is dedicated to one year, and it presents algorithms of the RED family, scientific publications (articles in scientific journals, conference proceedings, technical reports, etc.) on which were presented this year. In Section 11 the authors discussed the results and the future research directions are highlighted. 2. 2016 The new AQM based congestion control mechanism for 4G/LTE networks with a minimal adjustment to classical RED algorithm [3] was proposed in [4] and named Smart RED (SmRED). In this algorithm in order to achieve the optimal end-to-end performance by regulating the queue size the packet drop probability function was divided into two sections for distinguishing of different network loads and dynamical adaptation to the levels of network load. The novel version of RED, based on fuzzy logic, was introduced in [5] and named Fuzzy logic-based RED (FL-RED). In FL-RED the thresholds are dynamically increased or decreased according to fuzzy logic rules in order to utilize the resource effectively. The combined modified version of NLRED [6] and REDwM [7] algorithms, Non-linear RED with Weighted Moving Average (NLREDwM) was proposed and analyzed in [8]. In this algorithm the average queue length was defined by the first order difference equation. The new version of RED algorithm based on the optimized link state routing protocol (OLSR), named Optimized Link RED (OLRED), was described in [9]. OLRED algorithm selects optimal routing link periodically and calculates the average of queue size one time. The version of Weighted RED (WRED) [10] with implementation of the optimized link state routing protocol (OLSR) was also presented in [9] and named as Optimized Link WRED (OLWRED). OLWRED algorithm selects optimal routing link and calculates the average of queue size periodically. Thus OLWRED is more sensitive to average queue size. The OLWRED algorithm was presented in more detail in [11]. The improved variant of RED algorithm to stability and thus named Stability RED (S-RED) was described in [12]. S-RED was focused on improving linear structure of packet drop probability function (the quadratic function was proposed) and reducing the quantity of setting parameters (the parameters
×

About the authors

Ivan S. Zaryadov

RUDN University; Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences

Author for correspondence.
Email: zaryadov-is@rudn.ru
ORCID iD: 0000-0002-7909-6396

Candidate of Physical and Mathematical Sciences, Asssistant Professor of Department of Probability Theory and Cybersecurity

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation; 44 Vavilova St, bldg 2, Moscow, 119333, Russian Federation

Hilquias C.C. Viana

RUDN University

Email: hilvianamat1@gmail.com
ORCID iD: 0000-0002-1928-7641

Ph.D. student of Department of Probability Theory and Cyber Security, Institute of Computer Science and Telecommunications

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Anna V. Korolkova

RUDN University

Email: korolkova-av@rudn.ru
ORCID iD: 0000-0001-7141-7610

Candidate of Physical and Mathematical Sciences, Asssistant Professor of Department of Probability Theory and Cybersecurity, Institute of Computer Science and Telecommunications

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

Tatiana A. Milovanova

RUDN University

Email: milovanovata@rudn.ru
ORCID iD: 0000-0002-9388-9499

Candidate of Physical and Mathematical Sciences, Asssistant Professor of Department of Probability Theory and Cybersecurity, Institute of Computer Science and Telecommunications

6 Miklukho-Maklaya St, Moscow, 117198, Russian Federation

References

  1. Zaryadov, I. S.,Viana, H. C., Korolkova, A.V. & Milovanova, T. A. Chronology of the development of Active Queue Management algorithms of RED family. Part 1: from 1993 up to 2005. Discrete and Continuous Models and Applied Computational Science 31, 305-331. doi: 10.22363/2658-46702023-31-4-305-331 (2023).
  2. Zaryadov, I. S.,Viana, H. C., Korolkova, A.V. & Milovanova, T. A. Chronology of the development of Active Queue Management algorithms of RED family. Part 2: from 2006 up to 2015. Discrete and Continuous Models and Applied Computational Science 32, 18-37. doi: 10.22363/2658-46702024-32-1-18-37 (2024).
  3. Floyd, S. & Jacobson, V. Random early detection gateways for congestion avoidance. IEEE/ACM Transactions on Networking 1, 397-413. doi: 10.1109/90.251892 (1993).
  4. Paul, A. K., Kawakami, H., Tachibana, A. & Hasegawa, T. An AQM based congestion control for eNB RLC in 4G/LTE network in 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (IEEE, Vancouver, BC, Canada, 2016), 1-5. doi: 10.1109/CCECE.2016.7726792.
  5. Syed Masood, M. & Sheik Abdul Khader, P. Effective queue management using fuzzy logic for congestion control in delay-sensitive applications over mobile Ad hoc networks in Emerging Research in Computing, Information, Communication and Applications (eds Shetty, N. R., Prasad, N. H. & Nalini, N.) (Springer Singapore, Singapore, 2016), 385-395. doi: 10.1007/978-981-10-0287-8_36.
  6. Zhou, K.,Yeung, K. L. & Li,V. O. Nonlinear RED: A simple yet efficient active queue management scheme. Computer Networks 50, 3784-3794. doi: 10.1016/j.comnet.2006.04.007 (2006).
  7. Domańska, J., Domański, A., Augustyn, D. & Klamka, J. A RED modified weighted moving average for soft real-time application. International Journal of Applied Mathematics and Computer Science 24, 697-707. doi: 10.2478/amcs-2014-0051 (2014).
  8. Domański, A., Domańska, J. & Czachórski, T. The impact of the degree of self-similarity on the NLREDwM mechanism with drop from front strategy in Computer Networks (eds Gaj, P., Kwiecień, A. & Stera, P.) 608 (Springer International Publishing, Cham, 2016), 192-203. doi: 10.1007/978-3319-39207-3_17.
  9. Lafta, W., Jabbar, S., Kadhim, D. & Ma, G. Heterogeneous network performance improvement Uuing proposed OLRED and OLWRED strategies. International Journal of Future Computer and Communication 5, 199-204. doi: 10.18178/ijfcc.2016.5.5.471 (2016).
  10. Cisco Systems Inc, C. Cisco IOS 12.0 Quality of Service 288 pp. (Cisco Press, USA, 1999).
  11. Lafta,W., Jabbar, S., Kadhim, D. & Ma, G. OLWRED: Best Selected Strategy for DataTransmission in Heterogeneous Networks. International Journal of Computer Applications 152, 11-15. doi:10. 5120/ijca2016911781 (2016).
  12. Zhao, Y.-h., Zheng, X.-f. & Tu, X.-y. Research on the improved way of RED wlgorithm S-RED. International Journal of u- and e- Service Science and Technology 9, 375-384. doi: 10.14257/ijunesst. 2016.9.2.36 (2 2016).
  13. Abdel-jaber, H., Ababneh, J., Thabtah, F., Daoud, A. M. & Baklizi, M. Performance analysis of the proposed Adaptive Gentle Random Early Detection method under noncongestion and congestion situations in Digital Enterprise and Information Systems (eds Ariwa, E. & El-Qawasmeh, E.) 194 (Springer Berlin Heidelberg, Berlin, Heidelberg, 2011), 592-603. doi: 10.1007/978-3-642-226038_52.
  14. Baklizi, M. & Ababneh, J. Performance evaluation of the proposed Enhanced Adaptive Gentle Random Early Detection algorithm in congestion situations. International Journal of Current Engineering and Technology 6, 1658-1664 (2016).
  15. Hanaa, M., Gamal, A. & Samy, E.-D. Active Queue Management for congestion control: performance evaluation, new approach, and comparative study. International Journal of Computing and Network Technology 05, 37-49. doi: 10.12785/ijcnt/050201 (2017).
  16. Karmeshu, Patel, S. & Bhatnagar, S. Adaptive mean queue size and its rate of change: queue management with random dropping 2016. doi: 10.48550/arXiv.1602.02241. arXiv: 1602.02241 [cs.NI].
  17. Karmeshu, Patel, S. & Bhatnagar, S. Adaptive mean queue size and its rate of change: queue management with random dropping. Telecommunication Systems 65, 281-295. doi:10.1007/ s11235-016-0229-4 (2017).
  18. Bhatnagar, S., Patel, S. & Karmeshu. A stochastic approximation approach to active queue management. Telecommunication Systems 68, 89-104. doi: 10.1007/s11235-017-0377-1 (2018).
  19. Abbasov, B. & Korukoglu, S. Effective RED: An algorithm to improve RED’s performance by reducing packet loss rate. Journal of Network and Computer Applications 32, 703-709. doi:10. 1016/j.jnca.2008.07.001 (2009).
  20. Khatari, M. & Samara, G. Congestion control approach based on Effective Random Early Detection and fuzzy logic 2017. doi: 10.48550/arXiv.1712.04247. arXiv: 1712.04247 [cs.NI].
  21. Fgee, E.-B., Smeda, A. & AbouElgaseem, K. MRED: An algorithm to insure high QoS in IP networks. Journal of Communications 12, 200-206. doi: 10.12720/jcm.12.4.200-206 (2017).
  22. Gyasi-Agyei, A. Service differentiation in wireless Internet using multiclass RED with drop threshold proportional scheduling in Proceedings 10th IEEE International Conference on Networks (ICON 2002). Towards Network Superiority (IEEE, Singapore, 2002), 175-180. doi: 10.1109/ICON.2002.1033307.
  23. Alkharasani, A. M., Othman, M., Abdullah, A. & Lun, K. Y. An improved Quality-of-Service performance using RED’s Active Queue Management flow control in classifying networks. IEEE Access 5, 24467-24478. doi: 10.1109/ACCESS.2017.2767071 (2017).
  24. Zhao, Y., Ma, Z., Zheng, X. & Tu, X. An improved algorithm of Nonlinear RED based on membership cloud theory. Chinese Journal of Electronics 26, 537-543. doi: 10.1049/cje.2017.03.013 (2017).
  25. Paul, A. K., Kawakami, H., Tachibana, A. & Hasegawa, T. Effect of AQM-based RLC buffer management on the eNB scheduling algorithm in LTE network. Technologies 5. doi:10.3390/ technologies5030059 (2017).
  26. Feng, C.-W., Huang, L.-F., Xu, C. & Chang, Y.-C. Congestion Control Scheme Performance Analysis Based on Nonlinear RED. IEEE Systems Journal 11, 2247-2254. doi: 10.1109/JSYST.2014. 2375314 (2017).
  27. Patel, Z. M. Queue occupancy estimation technique for adaptive threshold based RED in 2017 IEEE International Conference on Circuits and Systems (ICCS) (IEEE, Thiruvananthapuram, India, 2017), 437-440. doi: 10.1109/ICCS1.2017.8326038.
  28. Aweya, J., Ouellette, M. & Montuno, D. Y. A control theoretic approach to active queue management. Computer Networks 36. Theme issue: Overlay Networks, 203-235. doi:10.1016/ S1389-1286(00)00206-1 (2001).
  29. Rezaee, A. A. & Pasandideh, F. A fuzzy congestion control protocol based on Active Queue Management in wireless sensor networks with medical applications. Wireless Personal Communications 98, 815-842. doi: 10.1007/s11277-017-4896-6 (2018).
  30. Floyd, S., Gummadi, R. & Shenker, S. Adaptive RED: An Algorithm for Increasing the Robustness of RED’s Active Queue Management tech. rep. (AT&T Center for Internet Research at ICSI, 2001).
  31. Ahmed, A. & Nasrelden, N. New congestion control algorithm to improve computer networks performance in 2018 International Conference on Innovative Trends in Computer Engineering (ITCE) (IEEE, Aswan, Egypt, 2018), 87-93. doi: 10.1109/ITCE.2018.8316605.
  32. Mahajan, M. & Singh, T. P. The Modified Gaussian function based RED (MGF-RED) algorithm for congestion avoidance in mobile Ad hoc networks. International Journal of Computer Applications 91, 39-44. doi: 10.5120/15889-5112 (2014).
  33. Soni, H. & Mishra, P. Reducing packet loss in Active Queue Management. International Journal of Computer Applications 81, 25-28. doi: 10.5120/14208-2447 (Nov. 2013).
  34. Akshatha, R. & Vedananda, D. E. Implementation of Hybrid Modified RED algorithm for congestion avoidance in MANETS. International Journal for Research in Applied Science and Engineering Technology 6, 2414-2419. doi: 10.22214/ijraset.2018.5396 (2018).
  35. Sharma, N., Rajput, S. S., Dwivedi, A. K. & Shrimali, M. P-RED: Probability based Random Early Detection algorithm for queue management in MANET in Advances in Computer and Computational Sciences (eds Bhatia, S. K., Mishra, K. K., Tiwari, S. & Singh, V. K.) 554 (Springer Singapore, Singapore, 2018), 637-643. doi: 10.1007/978-981-10-3773-3_62.
  36. Baklizi, M., Abdel-Jaber, H., Abualhaj, M., Abdullah, N., Ramadass, S. & Almomani, D. Dynamic stochastic early discovery: A new congestion control technique to improve networks performance. International Journal of Innovative Computing Information and Control IJICIC 9, 1113-1126 (2013).
  37. Baklizi, M., Ababneh, J., Abualhaj, M. M., Abdullah, N. & Abdullah, R. Markov-modulated bernoulli dynamic gentle random early detection. Journal of Theoretical and Applied Information Technology 96, 6688-6698 (2018).
  38. Chhabra, K., Kshirsagar, M. & Zadgaonkar, A. An improved RED algorithm with input sensitivity in Cyber Security (eds Bokhari M. U.and Agrawal, N. & Saini, D.) 729 (Springer Singapore, Singapore, 2018), 35-45. doi: 10.1007/978-981-10-8536-9_5.
  39. Abualhaj, M. M., Abu-Shareha, A. A. & Al-Tahrawi, M. M. FLRED: an efficient fuzzy logic based network congestion control method. Neural Computing and Applications 30, 925-935. doi: 10.1007/s00521-016-2730-9 (2018).
  40. Kachhad1, K. & Lathigara, A. ModRED : Modified RED an efficient congestion control algorithm for wireless network. International Research Journal of Engineering and Technology IRJETs 5, 1879- 1884 (2018).
  41. Su, Y., Huang, L. & Feng, C. QRED: A Q-learning-based Active Queue Management scheme. Journal of Internet Technology 19, 1169-1178. doi: 10.3966/160792642018081904019 (2018).
  42. Cisco Systems Inc, C. Cisco IOS Quality of Service Solutions Configuration Guide, Release 12.2 tech. rep. (Cisco, 1999).
  43. Alhassan, M. S. E. & Hagras, H. Towards congestion control approach based on Weighted Random Early Detection and type-2 fuzzy logic system in 2018 10th Computer Science and Electronic Engineering (CEEC) (IEEE, Colchester, UK, 2018), 71-74. doi: 10.1109/CEEC.2018.8674190.
  44. Alhassan, M. S. E. & Hagras, H. A congestion control approach based on Weighted Random Early Detection and type-2 fuzzy logic system. International Journal of Computer Science Trends and Technology IJCST 8, 83-94. doi: 10.33144/23478578/IJCST-V8I4P14 (2020).
  45. Monisha, V. & Ranganayaki, T. Congestion Avoidance Aware using Modified Weighted Fairness Guaranteed DRED-FDNNPID Congestion Control for MWSN in 2018 Tenth International Conference on Advanced Computing (ICoAC) (IEEE, Chennai, India, 2018), 133-137. doi: 10.1109/ICoAC44903.2018.8939080.
  46. Chen, L. & Cao, J. AdaptivecongestioncontrolofInternetofThingsbasedonImprovedREDAlgorithm in 2018 Chinese Automation Congress (CAC) (IEEE, Xi’an, China, 2018), 295-298. doi:10.1109/ CAC.2018.8623124.
  47. Hamadneh, N., Obiedat, M., Qawasmeh, A. & Bsoul, M. HRED, An Active Queue Management algorithm for TCP congestion control. Recent Patents on Computer Science 12, 212-217. doi:10. 2174/2213275912666181205155828 (2019).
  48. Al-Allaf, A. & Jabbar, A. I. A. RED with reconfigurable maximum dropping probability. International Journal of Computing and Digital Systems 8, 61-72. doi: 10.12785/ijcds/080107 (2019).
  49. Floyd, S. Recommendation on using the “gentle variant of RED” tech. rep. (The ICSI Networking ans Security Gropup, 2000).
  50. Abdel-Jaber, H., Shehab, A., Barakat, M. & Rashad, M. IGRED: An Improved Gentle Random Early Detection method for management of congested networks. Journal of Interconnection Networks 19, 1950004. doi: 10.1142/S021926591950004X (June 2019).
  51. Abdel-Jaber, H., Alkhateeb, J. H. & El-Amir, M. Evaluation of the performance for IM-RED and IGRED algorithms using discrete-time queues in 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) (IEEE, Al-Khobar, Saudi Arabia, 2022), 23-28. doi: 10.1109/CICN56167.2022.10008318.
  52. Baklizi, M. Stabilizing average queue length in Active Queue Management method. International Journal of Advanced Computer Science and Applications 10, 77-83. doi: 10.14569/IJACSA.2019. 0100310 (2019).
  53. Adel, A. A.-S. Enhanced Random Early Detection using responsive congestion indicators. International Journal of Advanced Computer Science and Applications 10, 358-367. doi:10.14569/ IJACSA.2019.0100347 (2019).
  54. Dash, P., Barpanda, N. & Panda, M. Congestion control in cable network transmission using Novel RED algorithm. International Journal of Innovative Technology and Exploring Engineering 8, 2278-3075. doi: 10.35940/ijitee.J9562.0881019 (Aug. 2019).
  55. Suwannapong, C. & Khunboa, C. Congestion control in CoAP Observe Group Communication. Sensors 19. doi: 10.3390/s19153433 (2019).
  56. Adel, A. Controlling delay at the router buffer using Modified Random Early Detection. International Journal of Computer Networks and Communications 11, 63-75. doi: 10.5121/ijcnc.2019.11604 (2019).
  57. Danladi, S. B. & Ambursa, F. U. DyRED: An enhanced Random Early Detection based on a new adaptive congestion control in 2019 15th International Conference on Electronics, Computer and Computation (ICECCO) (IEEE, Abuja, Nigeria, 2019), 1-5. doi: 10.1109/ICECCO48375.2019. 9043276.
  58. Abdel-Jaber, H. An exponential Active Queue Management method based on Random Early Detection. Journal of Computer Networks and Communications 2020, 63-75. doi: 10.1155/2020/ 8090468 (2020).
  59. Adamu, A., Shorgin, V., Melnikov, S. & Gaidamaka, Y. Flexible Random Early Detection algorithm for queue management in routers in Distributed Computer and Communication Networks (eds Vishnevskiy, V. M., Samouylov, K. E. & Kozyrev, D. V.) 12563 (Springer International Publishing, Cham, 2020), 196-208. doi: 10.1007/978-3-030-66471-8_16.
  60. Li, S., Xu, Q., Gaber, J., Dou, Z. & Chen, J. Congestion control mechanism based on dual threshold DI-RED for WSNs. Wireless Personal Communications 115, 2171-2195. doi: 10.1007/s11277-020-07676-6 (2020).
  61. Al-Allaf, A. F. & Jabbar, A. I. A. Reconfigurable Nonlinear GRED algorithm. International Journal of Computing and Digital Systems 9, 1009-1022. doi: 10.12785/ijcds/090521 (2020).
  62. Xue, L. The implementation of an improved ARED congestion control algorithm in 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (IEEE, Hangzhou, China, 2020), 22-25. doi: 10.1109/ISCID51228.2020.00012.
  63. Baklizi, M. Weight Queue Dynamic Active Queue Management algorithm. Symmetry 12. doi:10. 3390/sym12122077 (2020).
  64. Kumhar, D., Kumar, A. & Kewat, A. QRED: an enhancement approach for congestion control in network communications. International Journal of Information Technology 13, 221-227. doi:10. 1007/s41870-020-00538-1 (2021).
  65. Adamu, A., Surajo, Y. & Jafar, M. T. SARED: A Self-Adaptive Active Queue Management scheme for improving quality of service in network systems. Computer Science 22, 253-267. doi:10.7494/ csci.2021.22.2.4020 (Apr. 2021).
  66. Suwannapong, C. & Khunboa, C. EnCoCo-RED: Enhanced congestion control mechanism for CoAP observe group communication. Ad Hoc Networks 112, 102377. doi: 10.1016/j.adhoc.2020. 102377 (2021).
  67. Singha, S., Jana, B., Jana, S. & Mandal, N. K. A novel congestion control clgorithm using buffer occupancy RED in Computational Intelligence in Pattern Recognition (eds Das, A. K., Nayak, J., Naik, B., Dutta, S. & Pelusi, D.) 1349 (Springer Singapore, Singapore, 2022), 519-528. doi:10. 1007/978-981-16-2543-5_44.
  68. Singha, S., Jana, B. & Mandal, N. K. Active Queue Management in RED considering critical point on target queue. Journal of Interconnection Networks 21, 2150017. doi: 10.1142/S0219265921500171 (2021).
  69. Feng, W.-C., Kandlur, D. D., Saha, D. & Shin, K. G. Techniques for Eliminating Packet Loss in Congested TCP/IP Networks tech. rep. (The University of Michigan, 1997).
  70. Singha, S., Jana, B., Mandal, N. K., Jana, S., Bandyopadhyay, S. & Midya, S. Application of dynamic weight with distance to reduce packet loss in RED based algorithm in Advanced Techniques for IoT Applications. EAIT 2021 (eds Mandal, J. K. & De, D.) 292 (Springer Singapore, Singapore, 2022), 530-543. doi: 10.1007/978-981-16-4435-1_52.
  71. Hassan, S., Oluwatope, A., Ajaegbu, C., Khadijha-Kuburat Adebisi, A. & Olasupo, A. QLREDActive Queue Management Algorithm. Journal of Computer Science and Its Application 28, 95-107. doi: 10.4314/jcsia.v28i1.8 (Sept. 2021).
  72. Jarrah, A., Alshiab, M. & Shurman, M. High performance Changeable Dynamic Gentle Random Early Detection (CDGRED) for congestion control at router buffer. International Journal of Grid and High Performance Computing 14, 1-14. doi: 10.4018/IJGHPC.301585 (Jan. 2022).
  73. Hassan, S. O., Ajaegbu, C., Ogunlere, S. O., Kanu, R. U. & Maitanmi, O. S. RED-I: a RED-based algorithm for Internet routers. Journal of Communications 17, 260-266. doi: 10.12720/jcm.17.4. 260-266 (2022).
  74. Bie, Y., Li, Z., Hu, Z. & Chen, J. Queue management algorithm for satellite networks based on traffic prediction. IEEE Access 10, 54313-54324. doi: 10.1109/ACCESS.2022.3163519 (2022).
  75. Jafri, S. T. A., Ahmed, I. & Ali, S. Queue-buffer optimization based on Aggressive Random Early Detection in massive NB-IoT MANET for 5G applications. Electronics 11, 2955. doi:10.3390/ electronics11182955 (2022).
  76. Hassan, S. O. RED-LE: a revised algorithm for Active Queue Management. Journal of Telecommunications and Information Technology 2, 91-97. doi: 10.26636/jtit.2022.160022 (2022).
  77. Hassan, S. O., Rufai, A. U., Ogunlere, S. O., Alao, O. D., Ogundele, L. A., Agbaje, M. O., Adegbenjo, A. A. & Kuyoro, S. O. I-RED: an improved Active Queue Management algorithm. Journal of Computer Science 18, 130-137. doi: 10.3844/jcssp.2022.130.137 (2022).
  78. Wei, D., Zheng, X.,Yan, Z. & Cai, R. AnactivequeuemanagementalgorithmtoenhanceREDstability in 2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) (IEEE, Wuhan, China, 2022), 524-529. doi: 10.1109/AEMCSE55572.2022. 00108.
  79. Sun, J., Zukerman, M. & Palaniswami, M. Stabilizing RED using a Fuzzy Controller in 2007 IEEE International Conference on Communications (IEEE, Glasgow, UK, 2007), 266-271. doi:10.1109/ ICC.2007.52.
  80. Baklizi, M., Abdel-Jaber, H., Adel, A., Abualhaj, M. & Ramadass, S. Fuzzy Logic Controller of Gentle Random Early Detection based on average queue length and delay rate. International Journal of Fuzzy Systems 16, 9-19 (2014).
  81. Abu-Shareha, A. A., Al-Kasasbeh, B., Shambour, Q. Y., Abualhaj, M. M. & Al-Khatib, S. N. Fuzzy comprehensive random early detection of router congestion. Information Technology and Control 51, 252-267. doi: 10.5755/j01.itc.51.2.30194 (2022).
  82. Duran, G., Valero, J., Amigó, J. M., Giménez, A. & Martínez-Bonastre, O. Stabilizing chaotic behavior of RED in 2018 IEEE 26th International Conference on Network Protocols (ICNP) (IEEE, Cambridge, UK, 2018), 241-242. doi: 10.1109/ICNP.2018.00033.
  83. Amigó, J. M., Duran, G., Giménez, A., Martínez-Bonastre, O. & Valero, J. Generalized TCP-RED dynamical model for Internet congestion control. Communications in Nonlinear Science and Numerical Simulation 82, 105075. doi: 10.1016/j.cnsns.2019.105075 (2020).
  84. Giménez, A., Murcia, M. A., Amigó, J. M., Martínez-Bonastre, O. & Valero, J. New RED-type TCPAQM algorithms based on beta distribution drop functions 2022. doi: 10.48550/arXiv.2201.01105. arXiv: 2201.01105.
  85. Giménez, A., Murcia, M. A., Amigó, J. M., Martínez-Bonastre, O. & Valero, J. New RED-type TCP-AQM algorithms based on beta distribution drop functions. Applied Sciences 12, 11176. doi: 10.3390/app122111176 (2022).
  86. Adel Abu-Shareha, A. Integrated random early detection for congestion control at the router buffer. Computer Systems Science and Engineering 40, 719-734. doi: 10.32604/csse.2022.018369 (2022).
  87. Hassan, S. O., Rufai, A. U., Agbaje, M. O., Enem, T. A., Ogundele, L. A. & Usman, S. A. Improved random early detection congestion control algorithm for internet routers. The Indonesian Journal of Electrical Engineering and Computer Science IJEECS 28, 384-395. doi: 10.11591/ijeecs.v28.i1.pp384-395 (2022).
  88. Hassan, S., Nwaocha, V., Rufai, A., Odule, T., Enem, T., Ogundele, L. & Usman, S. Random early detection-quadratic linear: an enhanced active queue management algorithm. Bulletin of Electrical Engineering and Informatics 11, 2262-2272. doi: 10.11591/eei.v11i4.3875 (2022).
  89. Mahawish, A. A. & Hassan, H. J. Improving RED algorithm congestion control by using the Markov decision process. Scientific Reports 12, 13363. doi: 10.1038/s41598-022-17528-x (2022).
  90. Abu-Shareha, A., Al-Kasasbeh, B., Shambour, Q. Y., Abualhaj, M. M., Alsharaiah, M. A. & Al-Khatib, S. N. Linear random early detection for congestion control at the router buffer. Informatica 46, 105-114. doi: 10.31449/inf.v46i5.3966 (2022).
  91. Karmeshu, Patel, S. & Bhatnagar, S. Adaptive mean queue size and its rate of change: queue management with random dropping 2016. doi: 10.48550/arXiv.1602.02241. arXiv: 1602.02241 [cs.NI].
  92. Karmeshu, Patel, S. & Bhatnagar, S. Adaptive mean queue size and its rate of change: queue management with random dropping. Telecommunication Systems 65, 281-295. doi:10.1007/ s11235-016-0229-4 (2022).
  93. Pan, C., Zhang, S., Zhao, C., Shi, H., Kong, Z. & Cui, X. A novel active queue management algorithm based on average queue length change rate. IEEE Access 10, 75558-75570. doi:10. 1109/ACCESS.2022.3189183 (2022).
  94. Patel, C. M. URED: upper threshold RED an efficient congestion control algorithm in 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (IEEE, Tiruchengode, India, 2013), 1-5. doi: 10.1109/ICCCNT.2013.6726469.
  95. Hassan, S., Rufai, A., Ajaegbu, C. & Ayankoya, F. DL-RED: a RED-based algorithm for routers. International Journal of Computer Applications in Technology 70, 244-253. doi: 10.1504/IJCAT. 2022.130879 (2022).
  96. Hassan, S., Rufai, A., Nwaocha, V., Ogunlere, S., Adegbenjo, A., Agbaje, M. & Aniemeka, E. Quadratic exponential random early detection: a new enhanced random early detectionoriented congestion control algorithm for routers. International Journal of Electrical and Computer Engineering IJECE 13, 669-679. doi: 10.11591/ijece.v13i1.pp669-679 (Feb. 2023).
  97. Hassan, S. & Rufai, A. Modified dropping-random early detection (MD-RED): a modified algorithm for controlling network congestion. International Journal of Information Technology 15, 1499-1508. doi: 10.1007/s41870-023-01201-1 (2023).
  98. Kato, K., Kato, H., Asahara, H., Ito, D. & Kousaka, T. Effects on random early detection of the packet drop probability function with an adjustable nonlinearity. Nonlinear Theory and Its Applications IEICE 14, 193-206. doi: 10.1587/nolta.14.193 (Apr. 2023).
  99. Abu-Shareha, A. A., Alsaaidah, A., Alshahrani, A. & Al-Kasasbeh, B. Fuzzy-based active queue management using precise fuzzy modeling and Genetic Algorithm. Symmetry 15. doi:10.3390/ sym15091733 (2023).
  100. Hou, K., Yang, J., Liu, F. & Zhang, C. An Active Queue Management Algorithm to Guarantee the QoS of LEO Satellite Network in 2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS) (IEEE, Chengdu, China, 2023), 1024-1031. doi: 10.1109/ISCTIS58954.2023.10213116.
  101. Kesiezie, K. & Murry, L. K. DREaD: Decision Tree-Aided Random Early Detection - An Intelligent Active Queue Management Technique in 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) (IEEE, Bangalore, India, 2023), 1-6. doi: 10.1109/SMARTGENCON60755.2023.10442782.
  102. Hassan, S. O., Solanke Olakunle O.and Odule, T. J., Adesina, A. O., Usman, S. A. & Ayinde, S. A. AmRED and RED-QE: redesigning random early detection algorithm. TelecommunicationSystems 85, 263-275. doi: 10.1007/s11235-023-01082-6 (2024).
  103. Zhang, C., Yang, J. & Wang, N. An active queue management for wireless sensor networks with priority scheduling strategy. Journal of Parallel and Distributed Computing 187, 104848. doi: 10.1016/j.jpdc.2024.104848 (2024).
  104. Hassan, S. O. AD-RED: A new variant of random early detection AQM algorithm. J. High Speed Netw. 30, 53-67. doi: 10.3233/JHS-222055 (Jan. 2024).
  105. Lhamo, O., Ma, M., Doan, T. V., Scheinert, T., Nguyen, G. T., Reisslein, M. & Fitzek, F. H. REDSP-CoDel: Random early detection with static priority scheduling and controlled delay AQM in programmable data planes. Computer Communications 214, 149-166. doi: 10.1016/j.comcom. 2023.11.026 (2024).
  106. Nichols, K. & Jacobson, V. Controlling queue delay. Commun. ACM 55, 42-50. doi:10.1145/ 2209249.2209264 (July 2012).
  107. Korolkova, A. V., Kulyabov, D. S. & Tchernoivanov, A. I. On the classification of RED algorithms. Russian. RUDN Journal of Mathematics Information Sciences and Physics 3, 34-46 (2009).
  108. Korolkova, A. V. & Zaryadov, I. S. The mathematical model of the traffic transfer process with a rate adjustable by RED in 2010 International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) (IEEE, Moscow, Russia, Oct. 2010), 1046-1050. doi: 10.1109/ICUMT.2010.5676505.
  109. Velieva, T. R., Korolkova, A. V. & Kulyabov, D. S. Designing installations for verification of the model of active queue management discipline RED in the GNS3 in The 6th International Congress on Ultra Modern Telecommunications and Control Systems. Saint-Petersburg, Russia. October 6-8, 2014 (IEEE Computer Society, 2015), 570-577. doi: 10.1109/ICUMT.2014.7002164.
  110. Korolkova, A. V., Kulyabov, D. S. & Sevastianov, L. A. Combinatorial and operator approaches to RED modeling. Mathematical Modelling and Geometry 3, 1-18. doi: 10.26456/mmg/2015-331 (2015).
  111. Korolkova, A. V. & Zaryadov, I. S. The mathematical model of the traffic transfer process with a rate adjustable by RED in International Congress on Ultra Modern Telecommunications and Control Systems (IEEE, Moscow, Russia, 2010), 1046-1050. doi: 10.1109/ICUMT.2010.5676505.
  112. Zaryadov, I. S., Korolkova, A. V., Kulyabov, D. S., Milovanova, T. & Tsurlukov, V. The survey on Markov-Modulated Arrival Processes and their application to the analysis of active queue management algorithms in Distributed Computer and Communication Networks. DCCN 2017. Communications in Computer and Information Science (eds Vishnevskiy, V. M., Samouylov, K. E. & Kozyrev, D. V.) 417-430 (Springer International Publishing, Cham, 2017). doi: 10.1007/978-3-319-66836-9_35.
  113. Viana C. C., H., Zaryadov, I. S., Tsurlukov, V. V., Milovanova, T. A., Bogdanova, E. V., Korolkova, A. V. & Kulyabov, D. S. The general renovation as the active queue management mechanism. Some aspects and results in Distributed Computer and Communication Networks. DCCN 2019 (eds Vishnevskiy, V., Samouylov, K. & Kozyrev, D.) 488-502 (Springer, Cham, 2019). doi:10.1007/ 978-3-030-36625-4_39.
  114. Apreutesey, A. M. Y., Korolkova, A. V. & Kulyabov, D. S. Modeling RED algorithm modifications in the OpenModelica in Proceedings of the Selected Papers of the 8th International Conference “Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems” (ITTMM-2019), Moscow, Russia, April 15-19, 2019 (eds Kulyabov, D. S., Samouylov, K. E. & Sevastianov, L. A.) 2407 (CEUR-WS, 2019), 5-14.
  115. Viana Carvalho Cravid, H., Zaryadov, I. S. & Milovanova, T. A. Queueing systems with different types of renovation mechanism and thresholds as the mathematical models of active queue management mechanism. Discrete and Continuous Models and Applied Computational Science 28, 305-318. doi: 10.22363/2658-4670-2020-28-4-305-318 (2020).
  116. Adams, R. Active queue management: a survey. Communications Surveys & Tutorials IEEE 15, 1425-1476. doi: 10.1109/SURV.2012.082212.00018 (2013).
  117. Abbas, G., Halim, Z. & Abbas, Z. H. Fairness-driven queue management: a survey and taxonomy. IEEE Communications Surveys & Tutorials 18, 324-367. doi: 10.1109/COMST.2015.2463121 (2016).

Copyright (c) 2024 Zaryadov I.S., Viana H.C., Korolkova A.V., Milovanova T.A.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies