Skip to main content
SpringerLink
Log in

Auction-Based Algorithms for Routing and Task Scheduling in Federated Networks

  • Published:
Journal of Network and Systems Management Aims and scope Submit manuscript

Abstract

This paper studies and develops multiple auction-based algorithms for resource exchange among decentralized systems in federated networks with distributed computational resources. Decentralized resource owners and users use processing, storage, and communication units to perform the available computational tasks at each time step while an auctioneer facilitates allocating resources. The auctioneer communicates with federates and receives bids for buying and selling resources, solves combinatorial problems, and proposes prices to federates. Multiple auction-based mechanisms are formulated and assessed using collective performance metrics in a networked federation. The auction-based algorithms include four reverse-bid and double-sided auctions: (1) first-price auction, (2) sequential non-linear pricing auction, (3) min–max closed-form pricing auction, and (4) balanced and maximizing closed-form pricing auction. For results, we assess algorithms for economic and computational efficiency using extensive simulation runs in hundreds of network topologies and initial conditions. The metrics introduced for our numerical validation include normalized bids and prices, collective values, and convergence rates.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.statsmodels.org/dev/anova.html.

References

  1. Johnson, S.: Star Trek: The Worlds of the Federation. Pocket Books, New York (1989)

    Google Scholar 

  2. Joseph, P., Carton, S.: The law of the federation: images of law, lawyers, and the legal system in Star Trek, the next generation. Univ. Toledo Law Rev. 24, 43 (1992)

    Google Scholar 

  3. Maier, M.W.: Architecting principles for systems-of-systems. Syst. Eng. 1(4), 267–284 (1998). https://doi.org/10.1002/(SICI)1520-6858(1998)1:4<267::AID-SYS3>3.0.CO;2-D

    Article  Google Scholar 

  4. Selva, D., Golkar, A., Korobova, O., Cruz, ILi, Collopy, P., de Weck, O.L.: Distributed earth satellite systems: what is needed to move forward? J. Aerosp. Inf. Syst. 14(8), 412–438 (2017). https://doi.org/10.2514/1.I010497

    Article  Google Scholar 

  5. Azevedo, J.L., Cunha, B., Almeida, L.: Hierarchical distributed architectures for autonomous mobile robots: a case study. In: Proc. 2007 IEEE Conf. Emerg. Technol. Fact. Autom. (EFTA), pp. 973–980. IEEE, New York (2007). https://doi.org/10.1109/EFTA.2007.4416889

  6. Petri, I., Beach, T., Zou, M., Montes, J.D., Rana, O., Parashar, M.: Exploring models and mechanisms for exchanging resources in a federated cloud. In: 2014 IEEE Int. Conf. Cloud Eng. (IC2E), pp. 215–224. IEEE, New York (2014). https://doi.org/10.1109/IC2E.2014.9

  7. Koponen, T., Casado, M., Thakkar, P., Zhang, R., Wendlandt, D.J.: Packet processing in federated network (2015). U.S. Patent 8,964,767

  8. Sun, J., Modiano, E., Zheng, L.: Wireless channel allocation using an auction algorithm. IEEE J. Sel. Areas Commun. 24(5), 1085–1096 (2006). https://doi.org/10.1109/JSAC.2006.872890

    Article  Google Scholar 

  9. Kutanoglu, E., David Wu, S.: On combinatorial auction and Lagrangean relaxation for distributed resource scheduling. IIE Trans. 31(9), 813–826 (1999). https://doi.org/10.1080/07408179908969883

    Article  Google Scholar 

  10. Zhang, H., Jiang, H., Li, B., Liu, F., Vasilakos, A.V., Liu, J.: A framework for truthful online auctions in cloud computing with heterogeneous user demands. IEEE Trans. Comput. 65(3), 805–818 (2016). https://doi.org/10.1109/TC.2015.2435784

    Article  MathSciNet  MATH  Google Scholar 

  11. Berhault, M., Huang, H., Keskinocak, P., Koenig, S., Elmaghraby, W., Griffin, P., Kleywegt, A.: Robot exploration with combinatorial auctions. In: 2003 IEEE/RSJ Int. Conf. Intell. Robots and Syst. (IROS), vol. 2, pp. 1957–1962. IEEE, New York (2003). https://doi.org/10.1109/IROS.2003.1248932

  12. Pica, U., Golkar, A.: Sealed-bid reverse auction pricing mechanisms for federated satellite systems. Syst. Eng. 20(5), 432–446 (2017). https://doi.org/10.1002/sys.21395

    Article  Google Scholar 

  13. Morgan, J.: Efficiency in auctions: theory and practice. J. Int. Money Financ. 20(6), 809–838 (2001). https://doi.org/10.1016/S0261-5606(01)00024-9

    Article  Google Scholar 

  14. Yi, C., Cai, J.: Ascending-price progressive spectrum auction for cognitive radio networks with power-constrained multiradio secondary users. IEEE Trans. Veh. Technol. 67(1), 781–794 (2017). https://doi.org/10.1109/TVT.2017.2744560

    Article  Google Scholar 

  15. Yi, C., Cai, J., Zhang, G.: Spectrum auction for differential secondary wireless service provisioning with time-dependent valuation information. IEEE Trans. Wireless Commun. 16(1), 206–220 (2016). https://doi.org/10.1109/TWC.2016.2621765

    Article  Google Scholar 

  16. Schaeffer-Filho, A., Lupu, E., Sloman, M.: Federating policy-driven autonomous systems: interaction specification and management patterns. J. Netw. Syst. Manag. 23(3), 753–793 (2015). https://doi.org/10.1007/s10922-014-9317-5

    Article  Google Scholar 

  17. Famaey, J., Latré, S., Wauters, T., De Turck, F.: End-to-end resource management for federated delivery of multimedia services. J. Netw. Syst. Manag. 22(3), 396–433 (2014). https://doi.org/10.1007/s10922-013-9288-y

    Article  Google Scholar 

  18. Jennings, B., Feeney, K., Fleck, J.J.: Managing federations and cooperative management. J. Netw. Syst. Manag. 22(3), 297–301 (2014). https://doi.org/10.1007/s10922-014-9308-6

    Article  Google Scholar 

  19. Umrao, S., Roy, A., Saxena, N., Singh, S., Jung, Ji: Mobile network operator and mobile user cooperation for customized d2d data services. J. Netw. Syst. Manag. 26(4), 878–903 (2018). https://doi.org/10.1007/s10922-018-9447-2

    Article  Google Scholar 

  20. Yi, C., Huang, S., Cai, J.: An incentive mechanism integrating joint power, channel and link management for social-aware D2D content sharing and proactive caching. IEEE Trans. Mobile Comput. 17(4), 789–802 (2017). https://doi.org/10.1109/TMC.2017.2741481

    Article  Google Scholar 

  21. Mo, J., Kim, W., Park, H.: Internet service pricing: flat or volume? J. Netw. Syst. Manag. 21(2), 298–325 (2013). https://doi.org/10.1007/s10922-012-9234-4

    Article  Google Scholar 

  22. Pastirčák, J., Friga, L., Kováč, V., Gazda, J., Gazda, V.: An agent-based economy model of real-time secondary market for the cognitive radio networks. J. Netw. Syst. Manag. 24(2), 427–443 (2016). https://doi.org/10.1007/s10922-015-9355-7

    Article  Google Scholar 

  23. Ehsanfar, A., Grogan, P.T.: Mechanism design for exchanging resources in federated networks. J. Netw. Syst. Manag. 1–25 (2019). https://doi.org/10.1007/s10922-019-09498-9

  24. Cong, L., Yang, H., Wang, Y., Di, X.: An auction-gaming based routing model for LEO satellite networks. In: Int. Conf. Mach. Learn. Intell. Commun., pp. 498–508. Springer, Berlin (2017). https://doi.org/10.1007/978-3-319-73447-7_54

  25. Kiran, Y.V., Giaffreda, R.: A dynamic pricing method for efficient radio resource management in wireless access networks. In: IEEE Int. Conf. Commun. (ICC), pp. 1–5. IEEE, New York (2011). https://doi.org/10.1109/icc.2011.5962681

  26. Jayaweera, S.K., Bkassiny, M., Avery, K.A.: Asymmetric cooperative communications based spectrum leasing via auctions in cognitive radio networks. IEEE Trans. Wireless Commun. 10(8), 2716–2724 (2011). https://doi.org/10.1109/TWC.2011.061311.102044

    Article  Google Scholar 

  27. Grosu, D., Chronopoulos, A.T.: Algorithmic mechanism design for load balancing in distributed systems. IEEE Trans. Syst. Man Cybern. Part B 34(1), 77–84 (2004). https://doi.org/10.1109/TSMCB.2002.805812

    Article  Google Scholar 

  28. Son, S., Sim, K.M.: A price-and-time-slot-negotiation mechanism for cloud service reservations. IEEE Trans. Syst. Man Cybern. Part B 42(3), 713–728 (2012). https://doi.org/10.1109/TSMCB.2011.2174355

    Article  Google Scholar 

  29. An, B., Lesser, V.: Characterizing contract-based multiagent resource allocation in networks. IEEE Trans. Syst. Man Cybern. Part B 40(3), 575–586 (2010). https://doi.org/10.1109/TSMCB.2009.2035100

    Article  Google Scholar 

  30. Zhang, Y., Lee, C., Niyato, D., Wang, P.: Auction approaches for resource allocation in wireless systems: a survey. IEEE Commun. Surv. Tutor. 15(3), 1020–1041 (2013). https://doi.org/10.1109/SURV.2012.110112.00125

    Article  Google Scholar 

  31. Zou, S., Ma, Z., Liu, X.: Auction-based mechanism for dynamic and efficient resource allocation. IEEE Trans. Syst. Man Cybern. Syst. 48(1), 34–49 (2018). https://doi.org/10.1109/TSMC.2016.2581025

    Article  Google Scholar 

  32. De Vries, S., Vohra, R.V.: Combinatorial auctions: a survey. INFORMS J. Comput. 15(3), 284–309 (2003). https://doi.org/10.1287/ijoc.15.3.284.16077

    Article  MathSciNet  MATH  Google Scholar 

  33. Shafiq, M., Choi, J.G.: Adaptive auction framework for spectrum market in cognitive radio networks. J. Netw. Syst. Manag. 26(2), 518–546 (2018). https://doi.org/10.1007/s10922-017-9429-9

    Article  Google Scholar 

  34. Pekeč, A., Rothkopf, M.H.: Combinatorial auction design. Manag. Sci. 49(11), 1485–1503 (2003). https://doi.org/10.1287/mnsc.49.11.1485.20585

    Article  MATH  Google Scholar 

  35. Zaman, S., Grosu, D.: Combinatorial auction-based allocation of virtual machine instances in clouds. J. Parallel Distrib. Comput. 73(4), 495–508 (2013). https://doi.org/10.1016/j.jpdc.2012.12.006

    Article  Google Scholar 

  36. Archer, A., Papadimitriou, C., Talwar, K., Tardos, É.: An approximate truthful mechanism for combinatorial auctions with single parameter agents. Internet Math. 1(2), 129–150 (2004). https://doi.org/10.1080/15427951.2004.10129086

    Article  MathSciNet  MATH  Google Scholar 

  37. Lehmann, D., Oćallaghan, L.I., Shoham, Y.: Truth revelation in approximately efficient combinatorial auctions. J. ACM 49(5), 577–602 (2002). https://doi.org/10.1145/585265.585266

    Article  MathSciNet  MATH  Google Scholar 

  38. Hajiesmaili, M.H., Deng, L., Chen, M., Li, Z.: Incentivizing device-to-device load balancing for cellular networks: an online auction design. IEEE J. Sel. Areas Commun. 35(2), 265–279 (2017). https://doi.org/10.1109/JSAC.2017.2659558

    Article  Google Scholar 

  39. Yi, C., Cai, J., Zhang, G.: Online spectrum auction in cognitive radio networks with uncertain activities of primary users. In: 2015 IEEE Int. Conf. Commun. (ICC), pp. 7576–7581. IEEE, New York (2015). https://doi.org/10.1109/ICC.2015.7249538

  40. Li, Z., Li, B., Zhu, Y.: Designing truthful spectrum auctions for multi-hop secondary networks. IEEE Trans. Mob. Comput. 14(2), 316–327 (2015). https://doi.org/10.1109/TMC.2013.64

    Article  Google Scholar 

  41. Mondal, A., Madria, S.K., Kitsuregawa, M.: ABIDE: a bid-based economic incentive model for enticing non-cooperative peers in mobile-P2P networks. In: Int. Conf. Database Syst. Adv. Appl., pp. 703–714. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-71703-4_59

  42. del Portillo, I., Cameron, B.G., Crawley, E.F.: A technical comparison of three low earth orbit satellite constellation systems to provide global broadband. Acta Astronautica 159, 123–135 (2019). https://doi.org/10.1016/j.actaastro.2019.03.040

    Article  Google Scholar 

  43. Brooks, D., Eddy, W.M., Clark III, G.J., Johnson, S.K.: In-space networking on NASA’s SCaN testbed. In: Proc. 34th AIAA Int. Commun. Satell. Syst. Conf., AIAA 2016-5754, pp. 445–449. AIAA (2016). https://doi.org/10.2514/6.2016-5754

  44. Anderegg, L., Eidenbenz, S.: Ad hoc-vcg: a truthful and cost-efficient routing protocol for mobile ad hoc networks with selfish agents. In: Proc. 9th Ann. Int. Conf. Mobile Comput. Netw., pp. 245–259. ACM (2003). https://doi.org/10.1145/938985.939011

  45. Han, X., Mandal, S., Pattipati, K.R., Kleinman, D.L., Mishra, M.: An optimization-based distributed planning algorithm: a blackboard-based collaborative framework. IEEE Trans. Syst. Man Cybern. Syst. 44(6), 673–686 (2014). https://doi.org/10.1109/TSMC.2013.2276392

    Article  Google Scholar 

  46. Wang, Y., Dai, W., Jin, Q., Ma, J.: BciNet: a biased contest-based crowdsourcing incentive mechanism through exploiting social networks. IEEE Trans. Syst. Man Cybern. Syst. 56, 56 (2018). https://doi.org/10.1109/TSMC.2018.2837165. (Early access)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vanguard, Enterprise Advice Data Analytics & AI, Malvern, PA, 19355, USA

    Abbas Ehsanfar

  2. School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Paul T. Grogan

Authors
  1. Abbas Ehsanfar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul T. Grogan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abbas Ehsanfar.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ehsanfar, A., Grogan, P.T. Auction-Based Algorithms for Routing and Task Scheduling in Federated Networks. J Netw Syst Manage 28, 271–297 (2020). https://doi.org/10.1007/s10922-019-09506-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10922-019-09506-y

Keywords

Search

Navigation