An OpenMP-Based Algorithmic Optimization for Congestion Control of Network Traffic

  • Monika Jain
  • Rahul Saxena
  • Vipul Agarwal
  • Alok Srivastava
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

The last decade being a web revolution in the field of electronic media, data and information exchange in various forms has significantly increased. With the advancement in the technological aspects of the communication mechanism, the textual form of data has taken the shape of audiovisual format, and more and more content over internet is being shared in this form. Data sharing in this form calls for the need of high bandwidth consumption which may slow down the network resulting in performance degradation of content delivery networks due to congestion. Several attempts have been made by the researchers to propose various techniques and algorithms to achieve optimal performance of the network resources under high-usage circumstances. But due to high-dense network architectures, the performance implementations of suggested algorithms for congestion may not be able to produce the desired results in real time. In this paper, we have presented an optimized multi-core architecture-based parallel version of two congestion control algorithms—leaky bucket and choke packet. The experimental results over a dense network show that optimized parallel implementation using OpenMP programming specification gets the network rebalancing in a very short span of time as compared to its serial counterpart. The proposed approach runs 60% faster than the serial implementation. The graphical map for the speed up continues to increase with the size of the network and routers. The paper throws the light on the implementation aspects as well as result analysis in detail along with some existing algorithms for the problem.

Keywords

Congestion control algorithm Leaky bucket Choke packet Multi-core High-performance computing 

References

  1. 1.
    Akiene, P.T., Kabari, L.G.: Simulation of an Optimized Data Packet Transmission in a Congested Network: Network and Complex Systems. ISSN 2224-610X (Paper) ISSN 2225-0603 (Online), vol. 5, no. 8, 2015Google Scholar
  2. 2.
    Swarna, M., Ravi, S., Anand, M.: Leaky bucket algorithm for congestion control. Int. J. Appl. Eng. Res. 11(5), 3155–3159 (2016). ISSN 0973-4562Google Scholar
  3. 3.
    Valenzuela, J.L., Monleon, A., San Esteban, I., Portoles, M., Sallent, O.: A hierarchical token bucket algorithm to enhance QoS in IEEE 802.11: proposal, implementation and evaluation. In: 2004 IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall, vol. 4, pp. 2659–2662. IEEE (2004)Google Scholar
  4. 4.
    Chandra, M.A., Kavitha, M.T.: Adaptive virtual queue with choke packets for congestion control in MANETs. Int. J. Comput. Netw. Wirel. Commun. (IJCNWC) ISSN: 2250-3501 (2014)Google Scholar
  5. 5.
    Ahmed, A.M., Paulus, R.: Congestion detection technique for multipath routing and load balancing in WSN. Wirel. Netw. 23(3), 881–888 (2017)CrossRefGoogle Scholar
  6. 6.
    Aramaki, T., Kinoshita, K., Tanigawa, Y., Tode, H., Watanabe, T.: A congestion control method for multiple services on shared M2M network. In: 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), pp. 873–877. IEEE (2016)Google Scholar
  7. 7.
    Choumas, K., Paschos, G.S., Korakis, T., Tassiulas, L.: Distributed load shedding with minimum energy. In: The 35th Annual IEEE International Conference on Computer Communications, IEEE INFOCOM 2016, pp. 1–9. IEEE (2016)Google Scholar
  8. 8.
    Zhou, D., Song, W., Cheng, Y.: A study of fair bandwidth sharing with AIMD-based multipath congestion control. IEEE Wirel. Commun. Lett. 2(3), 299–302 (2013)CrossRefGoogle Scholar
  9. 9.
    Ramakrishna, K., Floyd, S.: A proposal to add explicit congestion notification (ECN) to IP (No. RFC 2481) (1998)Google Scholar
  10. 10.
    Saxena, R., Jain, M., Sharma, D.P., Mundra, A.: A review of load flow and network reconfiguration techniques with their enhancement for radial distribution network. In: 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC), pp. 569–574. Waknaghat, (2016). http://www.doi.org/10.1109/PDGC.2016.7913188
  11. 11.
    Amir, E.: A map of the MBone: August 5th, 1996. http://www.cs.berkeley.edu/~elan/mbone.html
  12. 12.
  13. 13.
    Dike, D., Obiora, V., Eze, C.: Improving congestion control in data communication network using queuing theory model. IOSR J. Electr. Electron. Eng. 11(2), 49–53 (2016). ISSN: 2278-1676, p-ISSN: 2320-3331Google Scholar
  14. 14.
    Saxena, R., Jain, M., Bhadri, S., Khemka, S.: Parallelizing GA based heuristic approach for TSP over CUDA and OPENMP. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1934–1940, Udupi, (2017). http://www.doi.org/10.1109/ICACCI.2017.8126128
  15. 15.
    Saxena R., Jain M., Sharma D.P.: GPU-based parallelization of topological sorting. In: Somani A., Srivastava S., Mundra A., Rawat S. (eds) Proceedings of First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies, vol 79. Springer, Singapore (2018)CrossRefGoogle Scholar
  16. 16.
    Saxena, R., Jain, M., Singh, D., & Kushwah, A.: An enhanced parallel version of RSA public key crypto based algorithm using openMP. In: Proceedings of the 10th International Conference on Security of Information and Networks (pp. 37–42). ACM (2017, October)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Monika Jain
    • 1
  • Rahul Saxena
    • 1
  • Vipul Agarwal
    • 1
  • Alok Srivastava
    • 1
  1. 1.Department of Information TechnologyManipal University JaipurJaipurIndia

Personalised recommendations