Single rate based extended logarithmic multicast congestion control


Due to increased demand for video data by heterogeneous Internet users, the demand of multicasting is increasing day by day. Multicasting is an efficient group communication technique, which is widely used in various applications such as bloggers, Internet group, forums, conferences, YouTube and online TV. Because of the heterogeneous nature of receivers, the network become congested that results in high packet loss, less throughput and reduced QoS. The multicast congestion control seems to be an effective solution to tackle the congestion issue in which the reception rate is adjusted according to the feedback of receivers. This paper provides a new congestion control scheme for multicast communication called Extended Logarithmic Increase and Multiplicative Decrease (ELIMD) to reduce packet loss, increase throughput, QoS and fairness during group communication. The entire research work is classified and elaborated in the key components namely architecture, newly designed equations, and flow charts. Experimental validation in NS-2.35 has affirmed the efficiency of the proposed scheme ELIMD against the existing schemes.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13


  1. Akamatsu J, Matsushima K, Yamamoto M (2016) Equation-based multicast congestion control in data center networks. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS), pp 1–6

  2. Bouras C, Gkamas A, Kioumourtzis G (2008) Adaptive smooth multicast protocol for multimedia data transmission. In: International symposium on performance evaluation of computer and telecommunication systems, Edinburgh, UK, pp 16–18

  3. Byers GI, Kwon JW (2003) Smooth multirate multicast congestion control INFOCOM. In: Twenty-second annual joint conference of the IEEE computer and communications, San Francisco, CA, pp 1022–1032

  4. Chakravarthi R, Gomathy CA (2011) Fuzzy approach to detect and control congestion in wireless sensor networks. Indian J Comput Sci Eng (IJCSE) 3(3):476–483

    Google Scholar 

  5. Chen C, Qiu T, Hu J, Ren Z, Zhou Y, Sangaiah AK (2017) A congestion avoidance game for information exchange on intersections in heterogeneous vehicular networks. J Netw Comput Appl 85:116–126

    Article  Google Scholar 

  6. Fan R, Cheng SD, Lin Y (2004) WRHMCC: a new congestion control mechanism for multicast applications. In: IEEE global telecommunications conference, pp 2845–2849

  7. Gao Q, Tong W, Kausar S, Huang L, Shen C, Zheng S (2017) Congestion-aware multicast plug-in for an SDN network operating system. Comput Netw 125:53–63

    Article  Google Scholar 

  8. Gao X, Chen T, Chen Z, Chen G (2018) NEMO: novel and efficient multicast routing schemes for hybrid data center networks. Comput Netw 138:149–163

    Article  Google Scholar 

  9. Gevros P, Crowcroft J, Kirstein P, Bhatti S (2001) Congestion control mechanisms and the best effort service model. IEEE Netw 15(3):16–26

    Article  Google Scholar 

  10. Hai DT, Son LH, Vinh LT (2017) Novel fuzzy clustering scheme for 3D wireless sensor networks. Appl Soft Comput 54:141–149

    Article  Google Scholar 

  11. Jiang W, Ren F, Wang J (2018) Survey on link layer congestion management of lossless switching fabric. Comput Stand Interfaces 57:31–35

    Article  Google Scholar 

  12. Jin J, Palaniswami M, Yuan D, Dong YN, Moessner K (2017) Priority service provisioning and max–min fairness: a utility-based flow control approach. J Netw Syst Manage 25(2):397–415

    Article  Google Scholar 

  13. Kammoun W, Youssef H (2005) TFMCC-based on a new equation for multicast rate Control. In: 12th IEEE international conference on electronics, circuits and systems (ICECS), pp 1–4

  14. Kammoun W, Youssef H (2010) Equation-based end to end single rate multicast congestion control, in Springer. J Ann Telecommun 65:219–231

    Article  Google Scholar 

  15. Kang J, Zhang Y, Nath B (2007) TARA: topology-aware resource adaptation to alleviate congestion in sensor networks. IEEE Trans Parallel Distrib Syst 18(7):919–931

    Article  Google Scholar 

  16. Kapoor R, Gupta R, Son LH, Jha S, Kumar R (2019) Adaptive technique with cross correlation for lowering signal-to-noise ratio wall in sensor networks. Wirel Personal Commun 105:1–16

    Article  Google Scholar 

  17. Kumar S, Singh K (2012) Logarithmic based multicast congestion control mechanism. In: 2012 international conference on industrial and intelligent information (ICIII 2012) IPCSIT, vol 31. IACSIT Press, Singapore

  18. Leung YW (2000) Congestion control for multipoint video conferencing. IEEE Trans Circuits Syst Video Technol 10(5):715–724

    Article  Google Scholar 

  19. Liwen H, Jin Y (2015) Research on multicast congestion control. In: 2015 IEEE 12th international conference on ubiquitous intelligence and computing, pp 846–850

  20. Ma H, Meng X, Zhou L, Li H, Zhang X (2009) Fuzzy-logic-based adaption scheme for TFMCC. In: 2009 international conference on networking and digital society, pp 238–241

  21. Malekpour A, Carzaniga A, Pedone F (2014) End-to-end congestion control for content-based networks. In: IEEE 33rd international symposium on reliable distributed systems, pp 221–231

  22. Manjul M, Mishra R (2014) A new equation based single rate multicast congestion control. In: 2014 international conference on computing for sustainable global development (INDIACom), pp 927–933

  23. Matrawy A, Lambadaris I (2003) A survey of congestion control schemes for multicast video applications. IEEE Commun Surv Tutor 5(2):22–31

    Article  Google Scholar 

  24. Mehdizadeh A, Hashim F, Abdullah RSAR, Ali BM, Othman M, Khatun S (2014) Multicast-unicast data delivery method in wireless IPv6 networks. J Netw Syst Manage 22(4):583–608

    Article  Google Scholar 

  25. Palacios RH, Díaz AF, Anguita M, Ortega J, Rodríguez-Quintana C (2017) High-throughput multi-multicast transfers in data center networks. J Supercomput 73(1):152–163

    Article  Google Scholar 

  26. Paschos GS, Li CP, Modiano E, Choumas K, Korakis T (2015) In-network congestion control for multirate multicast. IEEE/ACM Trans Netw 24(5):3043–3055

    Article  Google Scholar 

  27. Pitsillides A, Sekercioglu A (2004) Fuzzy logic based congestion control. In: Information and communication technologies: from theory to applications, pp 373–374

  28. Puangpronpitag S (2007) Multi- rate multicast congestion control by explicit rate adjustment and multicast- encouraging b—friendliness, In: 15th IEEE international conference on networks, pp 101–106

  29. Qureshi KN, Abdullah AH, Kaiwartya O, Iqbal S, Butt RA, Bashir F (2017) A dynamic congestion control scheme for safety applications in vehicular ad hoc networks. Comput Electr Eng Comput Electr Eng 72:774–788

    Article  Google Scholar 

  30. Rajesh M, Gnanasekar JM (2017) Congestion control scheme for heterogeneous wireless ad hoc networks using self-adjust hybrid model. Int J Pure Appl Math 116:537–547

    Google Scholar 

  31. Ren Y, Li J, Shi S, Li L, Wang G, Zhang B (2016) Congestion control in named data networking—a survey. Comput Commun 86:1–11

    Article  Google Scholar 

  32. Rizzo L (2008) PGMCC: a TCP-friendly single rate multicast congestion control scheme. In: SIGCOMM, Sweden, pp 17–28

  33. Robinson YH, Julie EG, Saravanan K, Kumar R, Son LH (2019) FD-AOMDV: fault-tolerant disjoint ad hoc on-demand multipath distance vector routing algorithm in mobile ad hoc networks. J Ambient Intell Human Comput.

    Article  Google Scholar 

  34. Roy A, Acharya T, DasBit S (2017) Social-based congestion-aware multicast in delay tolerant networks. In: Proceedings of the ACM workshop on distributed information processing in wireless networks, pp 5

  35. Safi QGK, Luo S, Wei C, Pan L, Yan G (2018) Cloud-based security and privacy-aware information dissemination over ubiquitous VANETs. Comput Stand Interfaces 56:107–115

    Article  Google Scholar 

  36. Saravanan K, Anusuya E, Kumar R, Son LH (2018) Real-time water quality monitoring using internet of things in SCADA. Environ Monit Assess 190:556–572

    Article  Google Scholar 

  37. Shi S, Waldvogel M (2000) A rate-based end-to-end multicast congestion control protocol. In: Proceedings of the fifth symposium on computers and communications, Louis, pp 678–686

  38. Singh K, Yadav RS, Manjul M, Dhir R (2008) Bandwidth delay quality parameters based multicast congestion control. In: 2008 16th international conference on advanced computing and communications, pp 399-405

  39. Singh K, Singh K, Aziz A (2018) Congestion control in wireless sensor networks by hybrid multi-objective optimization algorithm. Comput Netw 138:90–107

    Article  Google Scholar 

  40. Son LH, Thong PH (2017) Soft computing methods for WiMax network planning on 3D geographical information systems. J Comput Syst Sci 83(1):159–179

    MathSciNet  Article  Google Scholar 

  41. Son LH, Jha S, Kumar R, Chatterjee JM, Khari M (2019) Collaborative handshaking approaches between internet of computing and internet of things towards a smart world: a review from 2009 to 2017. Telecommun Syst 70(4):617–634

    Article  Google Scholar 

  42. Tam NT, Hai DT, Son LH, Vinh LT (2018) Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization. Wirel Netw 24(5):1477–1490

    Article  Google Scholar 

  43. Tanaka D, Kawarasaki M (2016) Congestion control in named data networking. In: IEEE international symposium on local and metropolitan area networks (LANMAN), pp 1–6

  44. Wang X, Zhang X, Yang S, Xue X (2007) Design of a fairness guarantee mechanism based on network measurement. In: HASE, Plano TX, pp 425–426

  45. Yadav KA, Kumar S (2017, October) A review of congestion control mechanisms for wireless networks. In: IEEE 2nd international conference on communication and electronics systems (ICCES), pp 109–115

  46. Youssef H, Kammoun W (2008) Improving the performance of end-to-end multicast congestion control. In: INFOCOM, pp 1128–1132

  47. Yue S, Cao Y (2011) An Improved TFMCC protocol based on end to end unidirectional delay jitter communication technology (ICCT). In: IEEE 13th international conference on communication technology, pp 1028–1032

  48. Zhang Y, Kang J (2011) End-to-end channel capacity measurement for congestion control in sensor network. In: Rutgers University, pp 1–9

  49. Zhu L, Ansari N, Sahinoglu Z, Vetro A, Sun H (2003) Scalable layered multicast with explicit congestion notification. In: Proceedings of the international conference on information technology: coding and computing, pp 331–335

  50. Zhu L, Wu J, Jiang G, Chen L, Lam SK (2018) Efficient hybrid multicast approach in wireless data center network. Future Generation Comput Syst 83:27–36

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Pham Huy Thong.

Ethics declarations

Conflict of interests

The authors declare that they do not have any conflict of interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Manjul, M., Mishra, R., Singh, K. et al. Single rate based extended logarithmic multicast congestion control. J Ambient Intell Human Comput 11, 2779–2791 (2020).

Download citation


  • Congestion control
  • Single rate
  • Logarithmic multicast
  • Multicast communication