Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers

Abstract

We address the virtual machine placement problem that arises in Cloud Service Providers data centers. We purpose, a Multi-Objective Integer Linear Programming model which aims at optimizing simultaneously the number of hosted virtual machines, the resource wastage and the number of active physical machines (PM) in order to minimize power consumption. This new combination of objectives enables to maximize the client satisfaction rate with minimizing the data center (DC) operational costs. We modelize this problem with a multi-objective integer linear program and solve it through two different methods. The first method computes a unique solution for a given preference order over the objectives whereas the second computes a set of non-dominated solutions. Both methods are compared through extensive simulation scenarios. We consider two DC architectures: homogeneous DCs (i.e., a DC with PMs having the same amount of resources) and heterogeneous DCs. We study the impact of each DC configuration on the performances of the solutions. We show that the second method leads to solutions with a reduction of up to 30% over the number of used PMs and that the heterogeneous DCs outperforms the homogeneous one across all objectives.

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References

  1. 1.

    Buyya R, Yeo CS, 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 Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  2. 2.

    Lombardi F, Di Pietro R (2011) Secure virtualization for cloud computing. Netw Comput Appl 34(4):1113–11122

    Article  Google Scholar 

  3. 3.

    Zhang Q, Cheng L, Boutaba R (2010) Cloud computing, state-of-the-art and research challenges. Internet Serv Appl 1(1):7–18

    Article  Google Scholar 

  4. 4.

    Hoehl M (2020) Proposal for standard cloud computing security SLAs-key metrics for safeguarding confidential data in the cloud

  5. 5.

    Serrano D, Bouchenak S, Kouki Y, Ledoux T, Lejeune J, Sopena J, Arantes L, Sens P (2013) Towards QoS-oriented SLA guarantees for online cloud services. In: International symposium on cluster, cloud and grid computing (CCGrid). IEEE, pp 50–57

  6. 6.

    Tharam D, Chen W, Elizabeth CH (2014) Cloud computing: issues and challenges. In: International conference on advanced information networking and applications. IEEE, pp 27–33

  7. 7.

    Mell P, Grance T (2019) The NIST definition of cloud computing

  8. 8.

    Lopez-Pires F, Baran B (2015) Virtual machine placement literature. Computing Research Repository (CoRR). arXiv:1506.01509

  9. 9.

    NIST Cloud Computing Reference Architecture and Taxonomy Working Group (2019) Cloud computing service metrics description

  10. 10.

    Regaieg R, Koubáa M, Osei-Opokou E, Aguili T (2018) A two objective linear programming model for VM placement in heterogeneous data centers. In: International symposium on ubiquitous networking. Springer, pp 167–178

  11. 11.

    Cvetkovic D, Parmee I (1998) Evolutionary design and multi-objective optimisation. In: Proceedings of the 6th European congress on intelligent techniques and soft computing (EUFIT). pp 397–401

  12. 12.

    Stanimirovic I, Zlatanovic M, Petkovic M (2011) On the linear weighted sum method for multi-objective optimization. Facta Acta Univ 26(4):49–63

    MathSciNet  MATH  Google Scholar 

  13. 13.

    Laghrissi A, Taleb T (2018) A survey on the placement of virtual resources and virtual network functions. IEEE Commun Surv Tutor 21(2):1409–1434

    Article  Google Scholar 

  14. 14.

    Rodero I, Viswanathan H, Lee EK, Gamell M, Pompili D, Parashar M (2012) Energy-efficient thermal-aware autonomic management of virtualized HPC cloud infrastructure. Grid Comput 10(3):447–473

    Article  Google Scholar 

  15. 15.

    Attaoui W, Sabir E (2018) Multi-criteria virtual machine placement in cloud computing environments. A literature review. arXiv:1802.05113

  16. 16.

    Beloglazov A, Buyya R (2012) Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers. Concurr Comput Pract Exp 24(13):1397–1420

    Article  Google Scholar 

  17. 17.

    Yousafzai A, Gant A, Md NR (2017) Cloud resource allocation schemes: review, taxonomy, and opportunities. Knowl Inf Syst 50(2):347–381

    Article  Google Scholar 

  18. 18.

    Aydina N, Muterb I, Birbilc S-I (2019) Bin packing problem with time dimension: an application in cloud computing. Preprint submitted to Elsevier

  19. 19.

    Tang M, Pan S (2014) A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Process Lett 41:1–11

    Google Scholar 

  20. 20.

    Lu K, Yahyapour R, Wieder P, Kotsokalis, C, Yaqub E, Jehangiri A.I (2013) Qos-aware vm placement in multi-domain service level agreements scenarios. In: 6th international conference on cloud computing (CLOUD). IEEE, pp 661–668

  21. 21.

    Nair SJ, Nair TR (2019) Performance degradation assessment and VM placement policy in cloud. Electr Comput Eng 9(6):2088–8708

    Google Scholar 

  22. 22.

    Shi L, Butler B, Wang R, Botvich D, Jennings B (2012) Optimal placement of virtual machines with different placement constraints in IAAS clouds. In: Symposium on ICT and energy efficiency and workshop on information theory and security. pp 202–206

  23. 23.

    Addya SK, Turuk AK, Bibhudatta S (2017) Simulated annealing based VM placement strategy to maximize the profit for Cloud Service Providers. Eng Scie Technol 20(4):1249–1259

    Google Scholar 

  24. 24.

    Wenying Y, Chen Q (2014) Dynamic placement of virtual machines with both deterministic and stochastic demands for green cloud computing. Mathematical Problems in Engineering. Hindawi, pp 11

  25. 25.

    Mollamotalebi M, Hajireza S (2017) Multi-objective dynamic management of virtual machines in cloud environments. Cloud Comput 6(1):16

    Article  Google Scholar 

  26. 26.

    Sultan A, Hamdaoui B (2018) Energy-aware resource management framework for overbooked cloud data centers with SLA assurance. In: Global communications conference (GLOBECOM). IEEE, pp 1–6

  27. 27.

    Guerout T, Gaoua Y, Artigues C, Da Costa G, Lopez P, Monteil T (2017) Mixed integer linear programming for quality of service optimization in Clouds. In: Future generation computer systems. Elsevier, pp 1–17

  28. 28.

    Ihara D, Lopez-Pires F, Baran B (2015) Many-objective virtual machine placement for dynamic environments. In: The 8th international conference on utility and cloud computing (UCC). pp 75–79

  29. 29.

    Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. Comput Syst Sci 70(8):1230–1242

    MathSciNet  Article  Google Scholar 

  30. 30.

    Shi L, Butler B, Wang R, Botvich D, Jennings B (2013) Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds. In: International symposium on integrated network management. IEEE, pp 499–501

  31. 31.

    Luo J, Song W, Yin L (2018) Reliable virtual machine placement based on multi-objective optimization with traffic-aware algorithm in industrial cloud. IEEE Access 6:23043–23052

    Article  Google Scholar 

  32. 32.

    Patil JT, Adamuthe AC (2017) Solving multi-objective virtual machine placement in cloud computing using NSGA-II. In: National conference for engineering post graduate students RIT NConPG-17. pp 182–187

  33. 33.

    Ma F, Liu F, Liu Z (2012) Multi-objective optimization for initial virtual machine placement in cloud data center. Inf Comput Sci 9(16):5029–5038

    Google Scholar 

  34. 34.

    Xu J, Fortes JAB (2010) Multi-objective virtual machine placement in virtualized data center environments. In: Conference on cyber, physical and social computing green computing and communications. IEEE, pp 179–188

  35. 35.

    AWS (2020). https://aws.amazon.com/fr/ec2/instance-types/. Accessed Nov 2020

  36. 36.

    HPE ProLiant DL380 Gen9 Server (2020). https://www.itcreations.com/hp/HP-ProLiant-DL380-Gen9-Server.asp. Accessed Nov 2020

  37. 37.

    DELL PowerEDGE R440 Server (2020). https://i.dell.com/sites/doccontent/shared-content/data-sheets/en/Documents/poweredge-r440-spec-sheet.pdf. Accessed Nov 2020

  38. 38.

    HPE ProLiant DL580 Gen10 Server (2020). https://www.hpe.com/us/en/product-catalog/servers/proliant-servers/pip.specifications.hpe-proliant-dl580-gen10-server.1010192779.html. Accessed Nov 2020

  39. 39.

    DELL PowerEDGE R510 Server (2020). https://www.dell.com/support/home/fr/fr/frbsdt1/product-support/product/poweredge-r510/manuals. Accessed Nov 2020

  40. 40.

    MultiJuMP (2020). https://julialang.org/. Accessed Nov 2020

  41. 41.

    MultiJuMP (2020). https://github.com/anriseth/MultiJuMP.jl. Accessed Nov 2020

  42. 42.

    IBM CPLEX Optimizer (2020). http://www-01.ibm.com/software/commerce/optimization/cplex-optimizer/. Accessed Nov 2020

  43. 43.

    Bechikh S, Datta R, Gupta A (2016) Recent advances in evolutionary multi-objective optimization. Springer, Berlin

    Google Scholar 

  44. 44.

    Rachmawati L, Srinivasan D (2009) Multiobjective evolutionary algorithm with controllable focus on the knees of the pareto front. IEEE Trans Evol Comput 13(4):810–824

    Article  Google Scholar 

  45. 45.

    Bechikh S (2012) Incorporating decision maker’s preference information in evolutionary multi-objective optimization. A Ph.d., thesis University of Tunis

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Correspondence to Rym Regaieg.

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Regaieg, R., Koubàa, M., Ales, Z. et al. Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers. Computing (2021). https://doi.org/10.1007/s00607-021-00915-z

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Keywords

  • Virtual machine placement
  • MILP model
  • Weighted sum method
  • Knee point

Mathematics Subject Classification

  • 90C05