Advertisement

Noncooperative Optimization of Multi-user Request Strategy in Cloud Service Composition Reservation

  • Zheng Xiao
  • Yang Guo
  • Gang Liu
  • Jiayi Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11334)

Abstract

With the maturity of virtualization technology and service-oriented architecture, single cloud services have been difficult to satisfy cloud users’ increasingly complex demand. Cloud service composition has become a hot topic. Nevertheless, few researches consider the problem of competition of service compositions among multiple users and interaction between the user and the cloud provider. Aiming at this problem, a service composition reservation model of a cloud provider, a cloud broker and multiple users is provided in this paper. A utility function related to revenue, payoff and performance of service compositions is designed and each user expects to maximize it. We consider this optimization problem from the perspective of game theory, and model it as a non-cooperative game. The existence of Nash equilibrium solution of the game is proved and an iterative proximate algorithm (IPA) is proposed to compute it. A series of simulation experiments are conducted to verify the theoretical analysis and the performance of IPA algorithm. The results show IPA algorithm quickly converge to a relatively stable state, and improve the utility of the user and the resource utilization of the cloud provider.

Keywords

Service composition Non-cooperative game Nash equilibrium 

Notes

Acknowledgments

This work is partially supported by Natural Science Foundation of China (No. 61872129 and No. 61802444) and Doctoral Scientific Research Foundation of Central South University of Forestry and Technology (No. 2016YJ047).

References

  1. 1.
    Atzeni, I., Ordóñez, L.G., Scutari, G., Palomar, D.P., Fonollosa, J.R.: Demand-side management via distributed energy generation and storage optimization. IEEE Trans. Smart Grid 4(2), 866–876 (2016)CrossRefGoogle Scholar
  2. 2.
    Cao, J., Kai, H., Li, K., Zomaya, A.Y.: Optimal multiserver configuration for profit maximization in cloud computing. IEEE Trans. Parallel Distrib. Syst. 24(6), 1087–1096 (2013)CrossRefGoogle Scholar
  3. 3.
    Cao, J., Li, K., Stojmenovic, I.: Optimal power allocation and load distribution for multiple heterogeneous multicore server processors across clouds and data centers. IEEE Trans. Comput. 63(1), 45–58 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chen, H., Li, Y., Louie, R.H.Y., Vucetic, B.: Autonomous demand side management based on energy consumption scheduling and instantaneous load billing: an aggregative game approach. IEEE Trans. Smart Grid 5(4), 1744–1754 (2013)CrossRefGoogle Scholar
  5. 5.
    Fadlullah, Z.M., Quan, D.M., Kato, N., Stojmenovic, I.: GTES: an optimized game-theoretic demand-side management scheme for smart grid. IEEE Syst. J. 8(2), 588–597 (2014)CrossRefGoogle Scholar
  6. 6.
    Fanjiang, Y.Y., Yang, S.: Semantic-based automatic service composition with functional and non-functional requirements in design time: a genetic algorithm approach. Inf. Softw. Technol. 56(3), 352–373 (2014)CrossRefGoogle Scholar
  7. 7.
    Foster, I., Zhao, Y., Raicu, I., Lu, S.: Cloud computing and grid computing 360-degree compared. In: Grid Computing Environments Workshop GCE 2008 (2008)Google Scholar
  8. 8.
    Kang, G., Liu, J., Tang, M., Liu, X., Fletcher, K.K.: Web service selection for resolving conflicting service requests. In: IEEE International Conference on Web Services, pp. 387–394 (2011)Google Scholar
  9. 9.
    Li, H., Zhu, Q., Ouyang, Y.: Non-cooperative game based QoS-aware web services composition approach for concurrent tasks. In: IEEE International Conference on Web Services, pp. 444–451 (2011)Google Scholar
  10. 10.
    Liu, C., Li, K., Xu, C., Li, K.: Strategy configurations of multiple users competition for cloud service reservation. IEEE Trans. Parallel Distrib. Syst. 27(2), 508–520 (2016)CrossRefGoogle Scholar
  11. 11.
    Mardukhi, F., Nematbakhsh, N., Barati, A., Barati, A.: QoS decomposition for service composition using genetic algorithm. Appl. Soft Comput. 13(7), 3409–3421 (2013)CrossRefGoogle Scholar
  12. 12.
    Pan, L., An, B., Liu, S., Cui, L.: Nash equilibrium and decentralized pricing for QoS aware service composition in cloud computing environments. In: IEEE International Conference on Web Services, pp. 154–163 (2017)Google Scholar
  13. 13.
    Samadi, P., Mohsenian-Rad, H., Schober, R., Wong, V.W.S.: Advanced demand side management for the future smart grid using mechanism design. IEEE Trans. Smart Grid 3(3), 1170–1180 (2012)CrossRefGoogle Scholar
  14. 14.
    Scutari, G., Palomar, D.P., Facchinei, F., Pang, J.S.: Convex optimization, game theory, and variational inequality theory. Signal Process. Mag. IEEE 27(3), 35–49 (2010)CrossRefGoogle Scholar
  15. 15.
    Scutari, G., Palomar, D.P., Facchinei, F., Pang, J.S.: Monotone Games for Cognitive Radio Systems. Springer, London (2012).  https://doi.org/10.1007/978-1-4471-2265-4_4CrossRefzbMATHGoogle Scholar
  16. 16.
    Shen, Y., Yang, X., Wang, Y., Ye, Z.: Optimizing QoS-aware services composition for concurrent processes in dynamic resource-constrained environments. In: IEEE International Conference on Web Services, pp. 250–258 (2012)Google Scholar
  17. 17.
    Simhon, E., Starobinski, D.: Game-theoretic analysis of advance reservation services. In: Information Sciences and Systems. pp. 1–6 (2014)Google Scholar
  18. 18.
    Wang, S., Sun, Q., Zou, H., Yang, F.: Particle swarm optimization with skyline operator for fast cloud-based web service composition. Mob. Netw. Appl. 18(1), 116–121 (2013)CrossRefGoogle Scholar
  19. 19.
    Yang, Y., Mi, Z., Sun, J.: Game theory based Iaas services composition in cloud computing environment. Adv. Inf. Sci. Serv. Sci. 4(22), 238–246 (2012)Google Scholar
  20. 20.
    Zhou, X., Li, K., Zhou, Y., Li, K.: Adaptive processing for distributed skyline queries over uncertain data. IEEE Trans. Knowl. Data Eng. 28(2), 371–384 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Computer Science and Electronic EngineeringHunan UniversityHunanChina
  2. 2.School of Computer and Information EngineeringCentral South University of Forestry and TechnologyHunanChina

Personalised recommendations