A market-oriented dynamic collaborative cloud services platform


Currently, interoperability and scalability are two major challenging issues for cloud computing. Forming a dynamic collaboration (DC) platform among cloud providers (CPs) can help to better address these issues. A DC platform can facilitate expense reduction, avoiding adverse business impacts and offering collaborative or portable cloud services to consumers. However, there are two major challenges involved in this undertaking; one is to find an appropriate market model to enable a DC platform, and the other one is to minimize conflicts among CPs that may occur in a market-oriented DC platform. In this paper, we present a novel combinatorial auction (CA)-based cloud market (CACM) model that enables a DC platform in CPs. To minimize conflicts among CPs, a new auction policy is proposed that allows a CP to dynamically collaborate with suitable partner CPs to form groups and publishes their group bids as a single bid to compete in the auction. However, identifying a suitable combination of CP partners to form the group and reduce conflicts is a NP-hard problem. Hence, we propose a promising multi-objective (MO) optimization model for partner selection using individual information and past collaborative relationship information, which is seldom considered. A multi-objective genetic algorithm (MOGA) called MOGA-IC is proposed to solve the MO optimization problem. This algorithm is developed using two popular MOGAs, the non-dominated sorting genetic algorithm (NSGA-II) and the strength pareto evolutionary genetic algorithm (SPEA2). The experimental results show that MOGA-IC with NSGA-II outperformed the MOGA-IC with SPEA2 in identifying useful pareto-optimal solution sets. Other simulation experiments were conducted to verify the effectiveness of the MOGA-IC in terms of satisfactory partner selection and conflict minimization in the CACM model. In addition, the performance of the CACM model was compared to the existing CA model in terms of economic efficiency.

This is a preview of subscription content, access via your institution.

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
Fig. 14
Fig. 15


  1. 1.

    Weiss A (2007) Computing in the clouds. netWorker 11(4):16–25

    Article  Google Scholar 

  2. 2.

    Interoperability (2009) A key challenge for cloud computing. http://www.lightreading.com/document.asp?doc_id=172033

  3. 3.

    Buyya R, Yeo CS, Venugopal S et al (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25:599–616

    Article  Google Scholar 

  4. 4.

    Yamazaki Y (2004) Dynamic collaboration: the model of new business that quickly responds to changes in the market through “The integrated IT/Network Solutions” provided by NEC. NEC Journal of Advanced Technology 1(1):9–16

    Google Scholar 

  5. 5.

    Bubendorfer, K. (2006) Fine grained resource reservation in open grid economies. In: Proceedings of the second IEEE international conference on e-science and grid computing, vol. 1, pp. 81–81

  6. 6.

    Bubendorfer, K, Thomson, W. (2006) Resource management using untrusted auctioneers in a grid economy. In: Proceedings of the second IEEE international conference on e-science and grid computing, vol. 1, pp. 74–74

  7. 7.

    Das A, Grosu D (2005) Combinatorial auction-based protocol for resource allocation in grid. In: Proceedings of the 19th IEEE international parallel and distributed processing symposium

  8. 8.

    Nepal, S., Zic, J. (2008) A conflict neighboring negotiation algorithm for resource services in dynamic collaboration. In: Proceedings of the IEEE international conference on services computing, vol. 2, pp. 7–11

  9. 9.

    Nepal, S., Zic, J., Chan, J. (2007) A distributed approach for negotiating resource contributions in dynamic collaboration. In: Proceedings of the 8th IEEE international conference on parallel and distributed computing applications and technologies, vol. 1, pp. 82–86

  10. 10.

    Ko CS, Kim T, Hwang H (2001) External partner selection using tabu search heuristics in distributed manufacturing. Int J Prod Res 39(17):3959–3974

    MATH  Article  Google Scholar 

  11. 11.

    Wu NQ, Su P (2005) Selection of partners in virtual enterprise paradigm. Robot Comput-Integr Manuf 21(5):119–131

    Article  Google Scholar 

  12. 12.

    Wang Z-J, Xu X-F et al (2009) Genetic algorithms for collaboration cost optimization-oriented partner selection in virtual enterprises. Int J Prod Res 47(4):859–881

    Article  Google Scholar 

  13. 13.

    Buyukozkan G, Feyzioglu O, Nebol E (2008) Selection of the strategic alliance partner in logistics value chain. Int J Prod Econ 113:148–158

    Article  Google Scholar 

  14. 14.

    Ip WH, Huang M, Yung KL et al (2003) Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise. Comput Oper Res 30:213–231

    MATH  Article  Google Scholar 

  15. 15.

    Fuqing Z, Yi H, Dongmei Y (2008) A multi-objective optimization model of the partner selection problem in a virtual enterprise and its solution with genetic algorithms. Int J Adv Manuf Technol 37:1220

    Article  Google Scholar 

  16. 16.

    Cheng F, Ye F, Yang J (2009) Multi-objective optimization of collaborative manufacturing chain with time-sequence constraints. Int J Adv Manuf Technol 40:1024–1032

    Article  Google Scholar 

  17. 17.

    Chen YL, Cheng LC, Chuang CN (2008) A group recommendation system with consideration of interactions among group members. Expert Syst Appl 34:2082–2090

    Article  Google Scholar 

  18. 18.

    Sha DY, Che ZH (2005) Virtual integration with a multi-criteria partner selection model for the multi-echelon manufacturing system. Int J Adv Manuf Technol 25:793–802

    Article  Google Scholar 

  19. 19.

    Amid A, Ghodsypour SH, Brien CO (2006) Fuzzy multi-objective linear model for supplier selection in a supply chain. Int J Prod Econ 104:394–407

    Article  Google Scholar 

  20. 20.

    Chang SL, Wang RC et al (2006) Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. Int J Prod Econ 100:348–359

    Article  Google Scholar 

  21. 21.

    Chen HH, Lee AHI, Tong Y (2007) Prioritization and operations NPD mix in a network with strategic partners under uncertainty. Expert Syst Appl 33:337–346

    Article  Google Scholar 

  22. 22.

    Saen RF (2007) Supplier selection in the presence of both cardinal and ordinal data. Eur J Oper Res 183:741–747

    MATH  Article  Google Scholar 

  23. 23.

    Huang XG, Wong YS, Wang JG (2004) A two-stage manufacturing partner selection framework for virtual enterprises. Int J Comput Integr Manuf 17(4):294–304

    Article  Google Scholar 

  24. 24.

    Gupta P, Nagi R (1995) Optimal partner selection for virtual enterprises in agile manufacturing. http://www.acsu.buffalo.edu/nagi/pubs/pgupta2.ps

  25. 25.

    Fischer M, Jahn H, Teich T (2004) Optimizing the selection of partners in production networks. Robot Comput-Integr Manuf 20(5):593–601

    Article  Google Scholar 

  26. 26.

    Kaya M (2009) MOGAMOD: multi-objective genetic algorithm for motif discovery. Expert Syst Appl 36:2

    Article  Google Scholar 

  27. 27.

    Cowan R, Jonard N et al (2007) Bilateral collaboration and the emergence of innovation networks. Manage Sci 53:1051–1067

    Article  Google Scholar 

  28. 28.

    Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197

    Article  Google Scholar 

  29. 29.

    Zitzler E, Laumanns M, Thiele L (2001) SPEA2: improving the strength Pareto evolutionary algorithm.TIK Report no. 103, Swiss Federal Institute of Technology

  30. 30.

    Grosu, D., Das, A (2004). Auction-based resource allocation protocols in grids. In: Proceedings of the 16th IASTED international conference on parallel and distributed computing and systems, vol. 1, pp. 20–27

  31. 31.

    Wolski R, Plank JS, Brevik J, Bryan T (2001) Analyzing market-based resource allocation strategies for the computational grid. Int J High Perform Comput Appl 15(3):258–281

    Article  Google Scholar 

  32. 32.

    Chen, S., Nepal, S., Wang, C., Zic. J. (2008) Facilitating dynamic collaborations with eContract services. In: Proceeding of 2008 IEEE international conference on Web services, vol. 1, pp. 521–528

  33. 33.

    Suzuki, K., Yokoo, M. (2003) Secure generalized Vickery auction using homomorphic encryption. In: Proceedings of 7th international conference on financial cryptography, LNCS, Springer, vol. 2742, pp. 239–249

  34. 34.

    Yokoo, M., Suzuki, K. (2002) Secure multi-agent dynamic programming based on homomorphic encryption and its application to combinatorial auctions. In: Proceedings of the first joint international conference on autonomous agents and multi-agent systems, vol. 1, ACM Press, pp. 112–119

  35. 35.

    Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, Berlin

    MATH  Google Scholar 

Download references


This work is supported by the MKE (The Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010-(C1090-1011-0001)).Corresponding author is Eui-Nam Huh.

Author information



Corresponding author

Correspondence to Eui-Nam Huh.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Hassan, M.M., Song, B. & Huh, EN. A market-oriented dynamic collaborative cloud services platform. Ann. Telecommun. 65, 669–688 (2010). https://doi.org/10.1007/s12243-010-0184-0

Download citation


  • Cloud market
  • Combinatorial auction
  • Dynamic collaboration
  • Interoperability
  • Partner selection
  • Multi-objective genetic algorithm