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Wireless Networks

, Volume 24, Issue 5, pp 1491–1508 | Cite as

A hierarchical approach for resource allocation in hybrid cloud environments

  • Zhe Liu
  • Changle Li
  • Weijie Wu
  • Riheng Jia
Article
  • 198 Downloads

Abstract

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.

Keywords

Stackelberg game Resource allocation Hybrid cloud 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61271176, 61401334, 61571350 and 61402287, the Fundamental Research Funds for the Central Universities (BDY021403), the 111 Project (B08038) and Shanghai Yangfan Project (No. 14YF1401900).

References

  1. 1.
    How Alibaba catered to USD 3 billion sales in a day. http://www.infoq.com/news/2012/12/interview-taobao-tmall.
  2. 2.
    de Castro Silva, J. L., Soma, N. Y., & Maculan, N. (2003). A greedy search for the three-dimensional bin packing problem: The packing static stability case. International Transactions in Operational Research, 10(2), 141–153.MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Panigrahy, R., Talwar, K., Uyeda, L., & Wieder, U. (2011). Heuristics for vector bin packing. Microsoft: Technical Report.Google Scholar
  4. 4.
    Osborne, M. J. (2004). An introduction to game theory. Oxford: Oxford University Press.Google Scholar
  5. 5.
    Wu, W., Lui, J. C., & Ma, R. T. (2013). On incentivizing upload capacity in P2P-VoD systems: Design, analysis and evaluation. Computer Networks, 57(7), 1674–1688.CrossRefGoogle Scholar
  6. 6.
    Jünger, M., Liebling, T. M., Naddef, D., et al. (2009). 50 Years of integer programming 1958–2008. NewYork: Springer.Google Scholar
  7. 7.
    Karp, R. M. (1972). Reducibility among combinatorial problems. In Complexity of computer computations series. The IBM research symposia series (pp. 85–103). Springer.Google Scholar
  8. 8.
    Fréville, A. (2004). The multidimensional 0–1 knapsack problem: An overview. European Journal of Operational Research, 155(1), 1–21.MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Puchinger, J., Raidl, G. R., & Pferschy, U. (2006). The core concept for the multidimensional knapsack problem. In Evolutionary computation in combinatorial optimization, series. Lecture Notes in Computer Science (vol. 3906, pp. 195–208).Google Scholar
  10. 10.
    Li, C., Liu, Z., Geng, X., Dong, M., Yang, F., Gan, X., et al. (2014). Two dimension spectrum allocation for cognitive radio networks. IEEE Transactions on Wireless Communications, 13(3), 1410–1423.CrossRefGoogle Scholar
  11. 11.
    Singh, A., Korupolu, M., & Mohapatra, D. (November 2008). Server-storage virtualization: integration and load balancing in data centers. In Proceedings of the ACM/IEEE conference on supercomputing (pp. 1–12).Google Scholar
  12. 12.
    Jameson, A., Schmidt, W., & Turkel, E. (June 1981). Numerical solution of the Euler equations by finite volume methods using Runge Kutta time stepping schemes. In Fluid and plasma dynamics conference (pp. 1–15).Google Scholar
  13. 13.
    Paruchuri, P., Pearce, J. P., Marecki, J., Tambe, M., Ordonez, F., & Kraus, S. (2008). Efficient algorithms to solve Bayesian Stackelberg games for security applications. In ACM AAMAS (pp. 895–902).Google Scholar
  14. 14.
    Montgomery, D. C., Runger, G. C., & Hubele, N. F. (2009). Engineering statistics (5th ed.). Hoboken: Wiley.Google Scholar
  15. 15.
    Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers (5th ed.). Hoboken: Wiley.zbMATHGoogle Scholar
  16. 16.
    Whitt, W. (2006). Staffing a calling center with uncertain arrival rate and absenteeism. Production and Operations Management, 15(1), 88–102.Google Scholar
  17. 17.
    Melvin, M., & Yin, X. (2000). Public information arrival, exchange rate volatility, and quote frequency. The Economic Journal, 110(465), 644–661.CrossRefGoogle Scholar
  18. 18.
    Xiao, Z., Song, W., & Chen, Q. (2013). Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Transactions on Parallel and Distributed Systems, 24(6), 1107–1117.CrossRefGoogle Scholar
  19. 19.
    Alicherry, M., & Lakshman, T. (2012). Network aware resource allocation in distributed clouds. In IEEE INFOCOM (pp. 963–971).Google Scholar
  20. 20.
    Ergu, D., Kou, G., Peng, Y., Shi, Y., & Shi, Y. (2013). The analytic hierarchy process: Task scheduling and resource allocation in cloud computing environment. The Journal of Supercomputing, 64(3), 835–848.CrossRefGoogle Scholar
  21. 21.
    Dán, G., & Carlsson, N. (2014). Dynamic content allocation for cloud-assisted service of periodic workloads. In IEEE INFOCOM (pp. 853–861).Google Scholar
  22. 22.
    Hao, F., Kodialam, M., Lakshman, T. V., & Mukherjee, S. (April 2014). Online allocation of virtual machines in a distributed cloud. In IEEE INFOCOM (pp. 10–18).Google Scholar
  23. 23.
    Shi, W., Zhang, L., Wu, C., Li, Z., & Lau, F. C. (June 2014). An online auction framework for dynamic resource provisioning in cloud computing. In ACM SIGMETRICS (pp. 71–83).Google Scholar
  24. 24.
    Armbrust, M., Fox, A., Griffith, R., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.CrossRefGoogle Scholar
  25. 25.
    Bittencourt, L. F., Madeira, E. R. M., & da Fonseca, N. L. S. (2012). Scheduling in hybrid clouds. IEEE Communications Magazine, 50(9), 42–47.CrossRefGoogle Scholar
  26. 26.
    Bittencourt, L. F., & Madeira, E. R. M. (2011). HCOC: A cost optimization algorithm for workflow scheduling in hybrid clouds. Journal of Internet Services and Applications, 2(3), 207–227.CrossRefGoogle Scholar
  27. 27.
    Lee, G., Chun, B., & Katz, R. H. (2011). Heterogeneity-aware resource allocation and scheduling in the cloud. In Proceedings of HotCloud (pp. 1–5).Google Scholar
  28. 28.
    den Bossche, R. V., Vanmechelen, K., & Broeckhove, J. (2010). Cost-optimal scheduling in hybrid IaaS clouds for deadline constrained workloads. In International conference on cloud computing (CLOUD) (pp. 228–235).Google Scholar
  29. 29.
    Zhou, Z., Zhang, H., Du, X., Li, P., & Yu, X. (2013). Prometheus: Privacy-aware data retrieval on hybrid cloud. In IEEE INFOCOM (pp. 2643–2651).Google Scholar
  30. 30.
    Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Integrated Services NetworksXidian UniversityShaanxiChina
  2. 2.Future Network Theory LabHuawei Technologies Co. LtdHong KongHong Kong
  3. 3.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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