Top

Journal of Network and Systems Management

Published in:

22-04-2019

Mechanism Design for Exchanging Resources in Federated Networks

Authors: Abbas Ehsanfar, Paul T. Grogan

Published in: Journal of Network and Systems Management | Issue 1/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This paper introduces a mechanism for pricing and exchanging resources in federated networks of task-processing elements. An operational model is developed to allocate processing, storage and communication resources to computational demands. This model finds an efficient and stable solution to combinatorial routing and allocating resources among networked elements with technical constraints. Using mixed-integer linear programming (MILP) formulation, we find optimal solution to processing tasks, allocating links, storing and delivering data to destination. A trusted auctioneer uses a mechanism to allocate resources to computational tasks and suggests prices for exchanging resources across a federation using minimum number of MILP solutions to a network topology. The proposed mechanism maximizes the collective value for a federation and ensures an expected value for each federate and minimizes the computational cost associated with the operational runs. The auctioneer doesn’t have access to utility functions and private information on resources a priori while assumes a federation with self-centric and rational participants. An application of federated satellite systems is developed with endogenous components such as adaptive bidding and opportunity cost of using resources. Numerical results show that the proposed mechanism improves the collective and expected values in a federation with strategic federates.

previous article Latency Control of ICN Enabled 5G Networks

next article Scalable Load Balancing and Flow Management in Dynamic Heterogeneous Wireless Networks

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

The availability of tasks in the contextual model and random actions adopted by a learning federate depend on a random seed.

https://gist.github.com/kastnerkyle/d127197dcfdd8fb888c2.

Wolverton, T.: Investors love Amazon’s cloud and advertising efforts, but it could have just gotten a big boost from an older business (2018). https://perma.cc/ZZ4N-QKQS. Accessed 24 Oct 2018

Hartmans, A.: The 10 billion club: meet the most valuable startups in the US (2017). https://perma.cc/2JGW-S3JV. Accessed 9 Dec 2017

Einav, L., Farronato, C., Levin, J.: Peer-to-peer markets. Annu. Rev. Econ. 8, 615–635 (2016). https://doi.org/10.1146/annurev-economics-080315-015334 CrossRef

Barnett, M.: The sharing economy: understanding the opportunities for growth (2017). https://newsroom.mastercard.com/eu/2017/06/26/the-sharing-economy-understanding-the-opportunities-for-growth/. Accessed 26 June 2017

Jennings, B., Stadler, R.: Resource management in clouds: survey and research challenges. J. Netw. Syst. Manag. 23(3), 567–619 (2015). https://doi.org/10.1007/s10922-014-9307-7 CrossRef

Sage, A.P., Cuppan, C.D.: On the systems engineering and management of systems of systems and federations of systems. Inf. Knowl. Syst. Manag. 2(4), 325–345 (2001)

Golkar, A., i Cruz, I.L.: The federated satellite systems paradigm: concept and business case evaluation. Acta Astronaut. 111, 230–248 (2015). https://doi.org/10.1016/j.actaastro.2015.02.009 CrossRef

Vaquero, L.M., Rodero-Merino, L., Caceres, J., Lindner, M.: A break in the clouds: towards a cloud definition. ACM SIGCOMM Comput. Commun. Rev. 39(1), 50–55 (2008). https://doi.org/10.1145/1496091.1496100 CrossRef

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 CrossRef

10.

Antoniadis, P., Fdida, S., Friedman, T., Misra, V.: Federation of virtualized infrastructures: sharing the value of diversity. In: Proceedings of 6th Conference on Emerging Network Experiment and Technology ACM (2010). https://doi.org/10.1145/1921168.1921184

11.

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 CrossRef

12.

Ehsanfar, A., Heydari, B.: An incentive-compatible scheme for electricity cooperatives: an axiomatic approach. IEEE Trans. Smart Grid (2016). https://doi.org/10.1109/TSG.2016.2591507 CrossRef

13.

Berman, M., Chase, J.S., Landweber, L., Nakao, A., Ott, M., Raychaudhuri, D., Ricci, R., Seskar, I.: Geni: a federated testbed for innovative network experiments. Comput. Netw. 61, 5–23 (2014). https://doi.org/10.1016/j.bjp.2013.12.037 CrossRef

14.

Holben, B.N., Eck, T.F., Slutsker, I., Tanré, D., Buis, J., Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y., Nakajima, T., Lavenu, F., Jankowiak, I., Smirnov, A.: AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 66(1), 1–16 (1998). https://doi.org/10.1016/S0034-4257(98)00031-5 CrossRef

15.

Grogan, P.T., Ho, K., Golkar, A., de Weck, O.L.: Multi-actor value modeling for federated systems. IEEE Syst. J. (2016). https://doi.org/10.1109/JSYST.2016.2626981 CrossRef

16.

Grogan, P.T., de Weck, O.L.: Interactive simulation games to assess federated satellite system concepts. In: Proceedings of 2015 IEEE Aerospace Conference, pp. 1–13. IEEE (2015). https://doi.org/10.1109/AERO.2015.7119101

17.

Selva, D., Collopy, P., de Weck, O.L.: Distributed earth satellite systems: what is needed to move forward? J. Aerosp. Comput. Inf. Commun. (2017). https://doi.org/10.2514/1.I010497 CrossRef

18.

Pantazis, N.A., Nikolidakis, S.A., Vergados, D.D.: Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Commun. Surv. Tutor. 15(2), 551–591 (2013). https://doi.org/10.1109/SURV.2012.062612.00084 CrossRef

19.

Albini, L.C.P., Caruso, A., Chessa, S., Maestrini, P.: Reliable routing in wireless ad hoc networks: the virtual routing protocol. J. Netw. Syst. Manag. 14(3), 335–358 (2006). https://doi.org/10.1007/s10922-006-9035-8 CrossRef

20.

Cesana, M., Cuomo, F., Ekici, E.: Routing in cognitive radio networks: challenges and solutions. Ad Hoc Netw. 9(3), 228–248 (2011). https://doi.org/10.1016/j.adhoc.2010.06.009 CrossRef

21.

Chabini, I.: Discrete dynamic shortest path problems in transportation applications: complexity and algorithms with optimal run time. Transp. Res. Rec. 1645, 170–175 (1998). https://doi.org/10.3141/1645-21 CrossRef

22.

Royer, E.M., Perkins, C.E.: Multicast operation of the ad-hoc on-demand distance vector routing protocol. In: Proceedings of 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking, pp. 207–218. ACM (1999). https://doi.org/10.1145/313451.313538

23.

Lee, S.J., Gerla, M., Chiang, C.C.: On-demand multicast routing protocol. In: Proceedings of 1999 IEEE Wireless Communications and Networking Conference, vol. 3, pp. 1298–1302. IEEE (1999). https://doi.org/10.1109/WCNC.1999.796947

24.

Kompella, V.P., Pasquale, J.C., Polyzos, G.C.: Optimal multicast routing with quality of service constraints. J. Netw. Syst. Manag. 4(2), 107–131 (1996). https://doi.org/10.1007/BF02139130 CrossRef

25.

Gombolay, M.C., Wilcox, R., Shah, J.A.: Fast scheduling of multi-robot teams with temporospatial constraints. In: Proceedings of Robotics: Science and Systems (2013)

26.

Werner, M.: A dynamic routing concept for ATM-based satellite personal communication networks. IEEE J. Sel. Areas Commun. 15(8), 1636–1648 (1997). https://doi.org/10.1109/49.634801 CrossRef

27.

Marce, O., Tran, H.H., Tuffin, B.: Double-sided auctions applied to vertical handover for mobility management in wireless networks. J. Netw. Syst. Manag. 22(4), 658–681 (2014). https://doi.org/10.1007/s10922-013-9269-1 CrossRef

28.

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/10.1023/A:1007666414678 CrossRef

29.

Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: Proceedings 2nd ACM Conference on Electronic Commerce, pp. 66–76. ACM (2000). https://doi.org/10.1145/352871.352879

30.

Xu, C., Song, L., Han, Z., Zhao, Q., Wang, X., Cheng, X., Jiao, B.: Efficiency resource allocation for device-to-device underlay communication systems: a reverse iterative combinatorial auction based approach. IEEE J. Sel. Areas Commun. 31(9), 348–358 (2013). https://doi.org/10.1109/JSAC.2013.SUP.0513031 CrossRef

31.

Li, C.F.: Cloud computing system management under flat rate pricing. J. Netw. Syst. Manag. 19(3), 305–318 (2011). https://doi.org/10.1007/s10922-010-9196-3 CrossRef

32.

Rebai, S.: Resource allocation in cloud federation. Ph.D. thesis, Institut National des Télécommunications (2017)

33.

Zhang, L., Li, Z., Wu, C.: Dynamic resource provisioning in cloud computing: a randomized auction approach. In: Proceeings of 2014 IEEE Conference on Computer Communications, pp. 433–441. IEEE (2014). https://doi.org/10.1109/INFOCOM.2014.6847966

34.

Liaqat, M., Chang, V., Gani, A., Ab Hamid, S.H., Toseef, M., Shoaib, U., Ali, R.L.: Federated cloud resource management: review and discussion. J. Netw. Comput. Appl. 77, 87–105 (2017). https://doi.org/10.1016/j.jnca.2016.10.008 CrossRef

35.

Do, C.T., Tran, N.H., Huh, E.N., Hong, C.S., Niyato, D., Han, Z.: Dynamics of service selection and provider pricing game in heterogeneous cloud market. J. Netw. Comput. Appl. 69, 152–165 (2016)CrossRef

36.

Toosi, A.N., Vanmechelen, K., Khodadadi, F., Buyya, R.: An auction mechanism for cloud spot markets. ACM Trans. Autom. Adapt. Syst. 11(1), 2 (2016). https://doi.org/10.1145/2843945 CrossRef

37.

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 CrossRef

38.

Kumar, D., Baranwal, G., Raza, Z., Vidyarthi, D.P.: A systematic study of double auction mechanisms in cloud computing. J. Syst. Softw. 125, 234–255 (2017). https://doi.org/10.1016/j.jss.2016.12.009 CrossRef

39.

Grogan, P.T., de Weck, O.L.: The ISoS modeling framework for infrastructure systems simulation. IEEE Syst. J. 9(4), 1139–1150 (2015). https://doi.org/10.1109/JSYST.2015.2420553 CrossRef

40.

Perez, R.E., Jansen, P.W., Martins, J.R.: pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization. Struct. Multidiscip. Optim. 45(1), 101–118 (2012). https://doi.org/10.1007/s00158-011-0666-3 MathSciNetCrossRefMATH

41.

Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992). https://doi.org/10.1007/BF00992698 CrossRefMATH

42.

Driessens, K., Ramon, J., Gärtner, T.: Graph kernels and Gaussian processes for relational reinforcement learning. Mach. Learn. 64(1), 91–119 (2006). https://doi.org/10.1007/s10994-006-8258-y CrossRefMATH

Title: Mechanism Design for Exchanging Resources in Federated Networks
Authors: Abbas Ehsanfar
Paul T. Grogan
Publication date: 22-04-2019
Publisher: Springer US
Published in: Journal of Network and Systems Management / Issue 1/2020
Print ISSN: 1064-7570
Electronic ISSN: 1573-7705
DOI: https://doi.org/10.1007/s10922-019-09498-9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2020

Event Detection and Multi-source Propagation for Online Social Network Management

Building Autonomic Elements from Video-Streaming Servers

Acknowledgment to Reviewers

Scalable Load Balancing and Flow Management in Dynamic Heterogeneous Wireless Networks

Security Aware Virtual Network Embedding Algorithm Using Information Entropy TOPSIS

Identify Critical Nodes in Network Cascading Failure Based on Data Analysis

Premium Partner