Cluster Computing

, Volume 22, Supplement 4, pp 8025–8033 | Cite as

A load balanced routing algorithm based on congestion prediction for LEO satellite networks

  • Houtian WangEmail author
  • Guoli Wen
  • Naijin Liu
  • Jun Zhang
  • Ying Tao


This paper presents a load balancing routing algorithm based on congestion prediction (LBRA-CP) so as to realize an efficient load balancing over the entire Low Earth Orbit satellite networks. A multi-objective optimization model is built, which not only adopts modifying factor to adjust path cost, but also uses congestion prediction to foresee inter-satellite link congestion. Then an ant colony algorithm is utilized to solve this model, resulting in finding an optimal path for every connection request. Meanwhile, in order to improve the reliability of LBRA-CP, the valve of the pheromone evaporation coefficient is discussed in this paper. The performance is measured by the receiver’s throughput, the link utilization and the end-to-end delay. Simulation results show that LBRA-CP performs well in balancing traffic load and increases the receiver’s throughput. Meanwhile, the end-to-end delay can meet the requirement of video transmission.


Ant-colony algorithm Congestion prediction LEO satellite networks Load balancing 



This work was supported by the National Natural Science Foundation of China under Grant Nos. 61571440 and 91438205.


  1. 1.
    Wen, G.L., Zhang, Q., Wang, H.T., et al.: An ant colony algorithm based on cross-layer design for routing and wavelength assignment in optical satellite networks. China Commun. 14(8), 63–75 (2017)CrossRefGoogle Scholar
  2. 2.
    Nishiyama, H., Tada, Y., Kato, N., et al.: Toward optimized traffic distribution for efficient network capacity utilization in two-layered satellite networks. IEEE Trans. Veh. Technol. 62(3), 1303–1313 (2013)CrossRefGoogle Scholar
  3. 3.
    Rao, Y., Wang, R.C.: Agent-based load balancing routing for LEO satellite networks. Comput. Netw. 54(17), 3187–3195 (2010)CrossRefGoogle Scholar
  4. 4.
    Wu, Z.F., Hu, G.Y., Jin, F.L., et al.: A novel routing design in the IP-based GEO/LEO hybrid satellite networks. Int. J. Satell. Commun. Netw. 35(3), 179–199 (2016)CrossRefGoogle Scholar
  5. 5.
    Ma, X.: Adaptive distributed load balancing routing mechanism for LEO satellite IP networks. J. Netw. 9(4), 816–822 (2014)Google Scholar
  6. 6.
    Song, G.H., Chao, M.Y., Yang, B.W., et al.: TLR: a traffic-light-based intelligent routing strategy for NGEO satellite IP networks. IEEE Trans. Wirel. Commun. 13(6), 3380–3393 (2014)CrossRefGoogle Scholar
  7. 7.
    Agnihotri, S., Ramkumar, K.R.: A survey and comparative analysis of the various routing protocols of Internet of things. Int. J. Pervasive Comput. Commun. 13(3), 264–281 (2017)CrossRefGoogle Scholar
  8. 8.
    Wang, H.T., Zhang, Q., Xin, X.J., et al.: Cross-layer design and ant-colony optimization based routing algorithm for Low Earth Orbit satellite networks. China Commun. 10(10), 37–46 (2013)CrossRefGoogle Scholar
  9. 9.
    Korcak, O., Alagoz, F., Jamalipour, A.: Priority-based adaptive routing in NGEO satellite networks. Int. J. Commun. Syst. 20(3), 313–333 (2007)CrossRefGoogle Scholar
  10. 10.
    Chang, H.S., Kim, B.W., Lee, C.G.: FSA-based link assignment and routing in low-earth orbit satellite networks. IEEE Trans. Veh. Technol. 47(3), 1037–1048 (1998)CrossRefGoogle Scholar
  11. 11.
    Papapetrous, E., Karapantazis, S., Pavlidou, F.N.: Distributed on-demand routing for LEO satellite systems. Comput. Netw. 51(15), 4356–4376 (2007)CrossRefGoogle Scholar
  12. 12.
    Kudoh, D., Kashibuchi, K., Nishiyama, H., et al.: Dynamic load balancing method based on congestion prediction for IP/LEO satellite networks. IEICE Trans. Commun. 92(11), 3326–3334 (2009)CrossRefGoogle Scholar
  13. 13.
    Xu, H., Wu, S.Q.: A distributed QoS routing based on ant algorithm for LEO satellite networks. Chin. J. Comput. 30(3), 361–367 (2007)Google Scholar
  14. 14.
    Royo, B., Sicilia, J.A., Oliveros, M.J., et al.: Solving a long-distance routing problem using ant colony optimization. Appl. Math. Inf. Sci. 9(2), 415–421 (2015)MathSciNetGoogle Scholar
  15. 15.
    Mohorcic, M., Svigelj, A., Kandus, G., Hu, Y.F., Sheriff, R.E.: Demographically weighted traffic flow models for adaptive routing in packet-switched non-geostationary satellite meshed networks. Comput. Netw. 43(2), 113–131 (2003)CrossRefGoogle Scholar
  16. 16.
    Yang, X., Sun, Z.L., Liu, H.F., et al.: Technology of new generation LEO satellite network and terrestrial MANET integration. ZTE Technol. J. 22(4), 58–63 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Houtian Wang
    • 1
    Email author
  • Guoli Wen
    • 2
  • Naijin Liu
    • 1
  • Jun Zhang
    • 1
  • Ying Tao
    • 3
  1. 1.Qian Xuesen Laboratory of Space TechnologyChina Academy of Space TechnologyBeijingChina
  2. 2.School of Electronic EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.China Academy of Space TechnologyBeijingChina

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