Optimal Resource Allocation Through Joint VM Selection and Placement in Private Clouds

  • Hongkun Chen
  • Feilong TangEmail author
  • Linghe Kong
  • Wenchao Xu
  • Xingjun Zhang
  • Yanqin Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11783)


It is the goal of private cloud platforms to optimize the resource allocation process and minimize the expense to process tasks. Essentially, resource allocation in clouds involves two phases: virtual machine selection (VMS) and virtual machine placement (VMP), and they can be jointly considered. However, existing solutions separate VMS and VMP, therefore, they can only get local optimal resource utilization. In this paper, we explore how to optimize the resource allocation globally through considering VMS and VMP jointly. Firstly, we formulate the joint virtual machine selection and placement (JVMSP) problem, and prove its NP hardness. Then, we propose the Resource-Decoupling algorithm that converts the JVMSP problem into two independent sub-problems: Max-Capability and Min-Cost. We prove that the optimal solutions of the two sub-problems guarantees the optimal solution of the JVMSP problem. Furthermore, we design the efficient Max-Balanced-Utility and Extent-Greedy heuristic algorithms to solve Max-Capability and Min-Cost, respectively. We evaluate our proposed algorithms on datasets with different distributions of resources, and the results demonstrate that our algorithms significantly improve the resource utilization efficiency compared with traditional solutions and existing algorithms.


Resource allocation VM selection VM placement Resource utilization efficiency Private clouds 



This work was supported in part by the National Natural Science Foundation of China projects under Grants 61832013 and 61672351, and in part by the Huawei Technologies Co., Ltd. project under Grant YBN2018125107.


  1. 1.
    Babu, K.R., Samuel, P.: Virtual machine placement for improved quality in IAAS cloud. In: 2014 Fourth International Conference on Advances in Computing and Communications, pp. 190–194. IEEE (2014)Google Scholar
  2. 2.
    Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28(5), 755–768 (2012)CrossRefGoogle Scholar
  3. 3.
    Blaisse, A.P., Wagner, Z.A., Wu, J.: Selection of virtual machines based on classification of MapReduce jobs. In: 2015 IEEE 35th International Conference on Distributed Computing Systems Workshops (ICDCSW), pp. 82–86. IEEE (2015)Google Scholar
  4. 4.
    Chaisiri, S., Lee, B.S., Niyato, D.: Optimal virtual machine placement across multiple cloud providers. In: IEEE Asia-Pacific Services Computing Conference, APSCC 2009, pp. 103–110. IEEE (2009)Google Scholar
  5. 5.
    Dashti, S.E., Rahmani, A.M.: Dynamic VMS placement for energy efficiency by PSO in cloud computing. J. Exp. Theor. Artif. Intell. 28(1–2), 97–112 (2016)CrossRefGoogle Scholar
  6. 6.
    Dell: Dell PowerEdge Servers. Accessed 4 Feb 2019
  7. 7.
    EC2, A.: Amazon EC2 instance types. Accessed 4 Feb 2019
  8. 8.
    Gahlawat, M., Sharma, P.: VM selection framework for market based federated cloud environment. In: 2015 International Conference on Computing, Communication and Automation, pp. 695–698. IEEE (2015)Google Scholar
  9. 9.
    Johnson, D.S.: Near-optimal bin packing algorithms (1973)Google Scholar
  10. 10.
    Li, Z., Shen, H., Miles, C.: PageRankVM: a PageRank based algorithm with anti-collocation constraints for virtual machine placement in cloud datacenters. In: 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS), pp. 634–644. IEEE (2018)Google Scholar
  11. 11.
    Melhem, S.B., Agarwal, A., Goel, N., Zaman, M.: Minimizing biased VM selection in live VM migration. In: 2017 3rd International Conference of Cloud Computing Technologies and Applications, pp. 1–7. IEEE (2017)Google Scholar
  12. 12.
    Reiss, C., Tumanov, A., Ganger, G.R., Katz, R.H., Kozuch, M.A.: Heterogeneity and dynamicity of clouds at scale: Google trace analysis. In: Proceedings of the Third ACM Symposium on Cloud Computing (SOCC), p. 7. ACM (2012)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Hongkun Chen
    • 1
  • Feilong Tang
    • 1
    Email author
  • Linghe Kong
    • 1
  • Wenchao Xu
    • 2
  • Xingjun Zhang
    • 3
  • Yanqin Yang
    • 2
  1. 1.Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina
  3. 3.School of Computer Science and TechnologyXi’an Jiaotong UniversityXianChina

Personalised recommendations