Advertisement

Multi-choice Virtual Machine Allocation with Time Windows in Cloud Computing

  • Jixian Zhang
  • Ning Xie
  • Xuejie Zhang
  • Weidong LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11204)

Abstract

Virtual machine allocation is a core problem in cloud computing. Most cloud computing platforms allow users to submit one requirement, which does not satisfy the diversity of user demands and also reduces the incomes of the platform. We propose a novel model, called multi-choice virtual machine allocation (MCVMA) with time windows, where the users can enter and leave the system at any time and submit multiple requirements. We design an optimal algorithm based on dynamic programming and a heuristic algorithm based on the resource scarcity and density for the MCVMA problem with time windows. We experimentally analyze both algorithms in terms of social welfare, execution time, resource utilization and users served.

Keywords

Cloud computing Multiple requirements Heuristic algorithm Online Virtual resource allocation 

Notes

Acknowledgments

The authors thank IBM for providing the full version of CPLEX12, which sets no limitations for solving for the optimal solution. This research is supported by the National Natural Science Foundation of China (61472345, 61662088 and 11663007), the Project of Natural Science Foundation of Yunnan Province of China (2014FA023, 2015FB115), and the Scientific Research Foundation of Yunnan Provincial Department of Education (2017ZZX228).

References

  1. 1.
    Amazon: Amazon ec2 instance types. https://amazonaws-china.com/cn/ec2/instance-types/
  2. 2.
    Angelelli, E., Bianchessi, N., Filippi, C.: Optimal interval scheduling with a resource constraint. Comput. Oper. Res. 51(3), 268–281 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Angelelli, E., Filippi, C.: On the complexity of interval scheduling with a resource constraint. Theoret. Comput. Sci. 412(29), 3650–3657 (2011)MathSciNetCrossRefGoogle Scholar
  4. 4.
    ASCI: Grid workloads archives. http://gwa.ewi.tudelft.nl
  5. 5.
    Darmann, A., Pferschy, U., Schauer, J.: Resource allocation with time intervals. Theoret. Comput. Sci. 411(49), 4217–4234 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Liu, X., Li, W., Zhang, X.: Strategy-proof mechanism for provisioning and allocation virtual machines in heterogeneous clouds. IEEE Trans. Parallel Distrib. Syst.  https://doi.org/10.1109/tpds.2017.2785815
  7. 7.
    Mashayekhy, L., Fisher, N., Grosu, D.: Truthful mechanisms for competitive reward-based scheduling. IEEE Trans. Comput. 65(7), 2299–2312 (2016)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Mashayekhy, L., Nejad, M.M., Grosu, D.: A PTAS mechanism for provisioning and allocation of heterogeneous cloud resources. IEEE Trans. Parallel Distrib. Syst. 26(9), 2386–2399 (2015)CrossRefGoogle Scholar
  9. 9.
    Nejad, M.M., Mashayekhy, L., Grosu, D.: Truthful greedy mechanisms for dynamic virtual machine provisioning and allocation in clouds. IEEE Trans. Parallel Distrib. Syst. 26(2), 594–603 (2015)CrossRefGoogle Scholar
  10. 10.
    Shi, W., Zhang, L., Wu, C., Li, Z., Lau, F.C.M.: An online auction framework for dynamic resource provisioning in cloud computing. IEEE/ACM Trans. Networking 24(4), 2060–2073 (2016)CrossRefGoogle Scholar
  11. 11.
    Zaman, S., Grosu, D.: Combinatorial auction-based allocation of virtual machine instances in clouds. J. Parallel Distrib. Comput. 73(4), 495–508 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jixian Zhang
    • 1
  • Ning Xie
    • 1
  • Xuejie Zhang
    • 1
  • Weidong Li
    • 2
    Email author
  1. 1.School of Information Science and EngineeringYunnan UniversityKunmingPeople’s Republic of China
  2. 2.School of Mathematics and StatisticsYunnan UniversityKunmingPeople’s Republic of China

Personalised recommendations