A Novel Market-Oriented Dynamic Collaborative Cloud Service Platform

  • Mohammad Mehedi Hassan
  • Eui-Nam Huh


In today’s world the emerging Cloud computing (Weiss, 2007) offer a new computing model where resources such as computing power, storage, online applications and networking infrastructures can be shared as “services” over the internet. Cloud providers (CPs) are incentivized by the profits to be made by charging consumers for accessing these services. Consumers, such as enterprises, are attracted by the opportunity for reducing or eliminating costs associated with “in-house” provision of these services.


Pareto Front Cloud Service Cloud Provider Service Requirement Combinatorial Auction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Amid, A., Ghodsypour, S. H., & Brien, C. O. (2006). Fuzzy multi-objective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104, 394–407.CrossRefGoogle Scholar
  2. Bubendorfer, K. (2006). Fine grained resource reservation in open grid economies. Proceedings of the 2nd IEEE International Conference on e-Science and Grid Computing, Vol. 1, Washington, DC, 81–81.Google Scholar
  3. Bubendorfer, K., & Thomson, W. (2006). Resource management using untrusted auctioneers in a grid economy. Proceedings of the 2nd IEEE International Conference on e-Science and Grid Computing, Vol. 1, Prentice Hall, NJ, 74–74.Google Scholar
  4. Buyukozkan, G., Feyzioglu, O., & Nebol, E. (2008). Selection of the strategic alliance partner in logistics value chain. International Journal of Production Economics, 113, 148–158.CrossRefGoogle Scholar
  5. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems, 25, 599–616.CrossRefGoogle Scholar
  6. Chang, S. L., Wang, R. C., & Wang, S. Y. (2006). Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle. International Journal of Production Economics, 100, 348–359.CrossRefGoogle Scholar
  7. Chen, Y. L., Cheng, L. C., & Chuang, C. N. (2008). A group recommendation system with consideration of interactions among group members. Expert Systems with Applications, 34, 2082–2090.CrossRefGoogle Scholar
  8. Chen, H. H., Lee, A. H. I., & Tong, Y. (2007). Prioritization and operations NPD mix in a network with strategic partners under uncertainty. Expert Systems with Applications, 33, 337–346.CrossRefGoogle Scholar
  9. Chen, S., Nepal, S., Wang, C. C., & Zic, J. (2008). Facilitating dynamic collaborations with eContract services. Proceeding of 2008 IEEE International Conference on Web Services, Vol. 1, Miami, FL, 521–528.Google Scholar
  10. Cheng, F., Ye, F., & Yang, J. (2009). Multi-objective optimization of collaborative manufacturing chain with time-sequence constraints. International Journal of Advanced Manufacturing Technology, 40, 1024–1032.CrossRefGoogle Scholar
  11. Cowan, R., Jonard, N., & Zimmermann, J. B. (2007). Bilateral collaboration and the emergence of innovation networks. Management Science, 53, 1051–1067.CrossRefGoogle Scholar
  12. Das, A., & Grosu, D. (2005). Combinatorial auction-based protocol for resource allocation in grid. Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium, Denver, CA.Google Scholar
  13. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197.CrossRefGoogle Scholar
  14. Fischer, M., Jahn, H., & Teich, T. (2004). Optimizing the selection of partners in production networks. Robotics and Comput-Integrated Manufacturing, 20(5), 593–601.CrossRefGoogle Scholar
  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. International Journal of Advanced Manufacturing Technology, 37, 1220.CrossRefGoogle Scholar
  16. Grosu, D., & Das, A. (2004). Auction-based resource allocation protocols in grids. Proceeding of the 16th IASTED International Conference on Parallel and Distributed Computing and Systems, Vol. 1, Los Angeles, CA, 20–27.Google Scholar
  17. Gupta, P., & Nagi, R. (1995). Optimal partner selection for virtual enterprises in agile manufacturing. Submitted to IIE Transactions on Design and Manufacturing, Special Issue on Agile Manufacturing.Google Scholar
  18. Huang, X. G., Wong, Y. S., & Wang, J. G. (2004). A two-stage manufacturing partner selection framework for virtual enterprises. International Journal of Computer Integrated Manufacturing, 17(4), 294–304.CrossRefGoogle Scholar
  19. Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Berlin: Springer.MATHCrossRefGoogle Scholar
  20. Coombe, B. (2009). Cloud Computing- Overview, Advantages and Challenges for Enterprise Deployment. Bechtel Technology Journal, 2(1).Google Scholar
  21. Ip, W. H., Huang, M., Yung, K. L., & Wang, D. (2003). Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise. Computers and Operations Research, 30, 213–231.MATHCrossRefGoogle Scholar
  22. Kaya, M. (2009). MOGAMOD: Multi-objective genetic algorithm for motif discovery. Expert Systems with Applications, 36, 2.CrossRefGoogle Scholar
  23. Ko, C. S., Kim, T., & Hwang, H. (2001). External partner selection using Tabu search heuristics in distributed manufacturing. International Journal of Production Research, 39(17), 3959–3974.MATHCrossRefGoogle Scholar
  24. Nepal, S., & Zic, J. (2008). A conflict neighboring negotiation algorithm for resource services in dynamic collaboration. Proceedings of the IEEE International Conference on Services Computing, 2, 7–11.Google Scholar
  25. Nepal, S., Zic, J., & Chan, J. (2007). A distributed approach for negotiating resource contributions in dynamic collaboration. Proceedings of the 8th IEEE International Conference on Parallel and Distributed Computing Applications and Technologies, Vol. 1, Genoa, Italy, 82–86.Google Scholar
  26. Saen, R. F. (2007). Supplier selection in the presence of both cardinal and ordinal data. European Journal of Operational Research, 183, 741–747.MATHCrossRefGoogle Scholar
  27. Sha, D. Y., & Che, Z. H. (2005). Virtual integration with a multi-criteria partner selection model for the multi-echelon manufacturing system. International Journal of Advanced Manufacturing Technology, 25, 793–802.CrossRefGoogle Scholar
  28. Suzuki, K., & Yokoo, M. (2003). Secure generalized vickery auction using homomorphic encryption. Proceedings of 7th International Conference on Financial Cryptography, LNCS, Springer, Vol. 2742, Hong-Kong, China, 239–249.Google Scholar
  29. Wang, Z.-J., Xu, X.-F., & Zhan, D. C. (2009). Genetic algorithms for collaboration cost optimization-oriented partner selection in virtual enterprises. International Journal of Production Research, 47(4), 859–881.Google Scholar
  30. Weiss, A. (December 2007). Computing in the clouds. netWorker, 11(4), 16–25.CrossRefGoogle Scholar
  31. Wolski, R., Plank, J. S., Brevik, J., & Bryan, T. (2001). Analyzing market-based resource allocation strategies for the computational grid. The International Journal of High Performance Computing Applications, 15(3), 258–281.CrossRefGoogle Scholar
  32. Wu, N. Q., & Su, P. (2005). Selection of partners in virtual enterprise paradigm. Robotics Computer-Integrated Manufacturing, 21(5), 119–131.CrossRefGoogle Scholar
  33. 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. NEC Journal of Advanced Technology, 1(1), 9–16.Google Scholar
  34. Yokoo, M., & Suzuki, K. (2002). Secure multi-agent dynamic programming based on homomorphic encryption and its application to combinatorial auctions. Proceedings of the First Joint International Conference on Autonomous Agents and Multi-agent Systems, ACM Press, Vol. 1, New York, NY, 112–119.Google Scholar
  35. Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm (TIK Rep. No. 103, Swiss Federal Institute of Technology, 2001).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer EngineeringKyung Hee UniversitySeoulSouth Korea

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