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The Journal of Supercomputing

, Volume 74, Issue 7, pp 2984–3015 | Cite as

A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study

  • Montassar Riahi
  • Saoussen Krichen
Article
  • 96 Downloads

Abstract

Cloud computing (CC) is the concept of accessing to computing resources: servers, networks, storage, and applications, on demand through a network. This new paradigm has led to the birth of several data centers worldwide offering cloud services across millions of virtual machines. In fact, virtual machine placement (VMP) is considered as one of the greatest challenges for cloud providers to optimize their platforms in terms of physical machines number which reduces power costs and resources wastage. In this work, we propose an efficient framework based on multi-objective genetic algorithm (GA) and Bernoulli simulation that aims to minimize simultaneously used hosts and resource wastage in each PM on a CC platform. We operationalized our GA in a real case study related to the real cloud platform of the Office of the Merchant Marine and Ports of Tunisia (OMMP). This framework not only helped this company to optimize the VMP of their outsourced backup site, but also to minimize the operating expenses dedicated to the target platform. The proposed algorithm is tested on the OMMP’s data center, and experimental results show that the proposed technique significantly outperforms the compared methods especially in terms of VMP quality.

Keywords

Cloud computing Virtual machine placement Genetic algorithm Bernoulli simulation Multi-objective Decision framework 

References

  1. 1.
    Singh S, Jeong Y, Park JH (2016) A survey on cloud computing security: issues, threats, and solutions. J Netw Comput Appl 75:200–222CrossRefGoogle Scholar
  2. 2.
    Javadia B, Abawajyb J, Buyya R (2012) Failure-aware resource provisioning for hybrid Cloud infrastructure. J Parallel Distrib Comput 72:1318–1331CrossRefGoogle Scholar
  3. 3.
    Laatikainen G, Mazhelis O, Tyrvainen P (2016) Cost benefits of flexible hybrid cloud storage: mitigating volume variation with shorter acquisition cycle. J Syst Softw 122:180–201CrossRefGoogle Scholar
  4. 4.
    Chung L, Hill T, Legunsen O, Sun Z, Dsouza A, Supakkul S (2013) A goal-oriented simulation approach for obtaining good private cloud-based system architectures. J Syst Softw 86:2242–2262CrossRefGoogle Scholar
  5. 5.
    De Coninck E, Verbelen T, Vankeirsbilck B, Bohez S, Simoens P, Dhoedt B (2016) Dynamic auto-scaling and scheduling of deadline constrained service workloads on IaaS clouds. J Syst Softw 118:101–114CrossRefGoogle Scholar
  6. 6.
    Manvi SS, Shyam GK (2014) Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J Netw Comput Appl 41:424–440CrossRefGoogle Scholar
  7. 7.
    Sharkh MA, Kanso A, Shami A, Öhlén P (2016) Building a cloud on earth: a study of cloud computing data center simulators. Comput Netw 108:78–96CrossRefGoogle Scholar
  8. 8.
    Gupta R, Kumar Bose S, Sundarrajan S, Chebiyam M, Chakrabarti A (2008) A two stage heuristic algorithm for solving the server consolidation problem with item–item and bin-item incompatibility constraints. In: Services Computing (2008) SCC08. IEEE International Conference, vol 2, pp 39–46Google Scholar
  9. 9.
    Benson T, Akella A, Maltz DA (2010) Network traffic characteristics of data centers in the wild. In: Proceedings of the 10th Annual Conference on Internet Measurement, pp 267–280Google Scholar
  10. 10.
    Luizelli MC, Bays LR, Buriol LS, Barcellos MP, Gaspary LP (2016) How physical network topologies affect virtual network embedding quality: a characterization study based on ISP and datacenter networks. J Netw Comput Appl 70:1–16CrossRefGoogle Scholar
  11. 11.
    Gupta R, Pateriya RK (2014) Survey on virtual machine placement techniques in cloud computing environment. Int J Cloud Comput Serv Architect (IJCCSA) 4(4):1–7CrossRefGoogle Scholar
  12. 12.
    Usmani Z, Singh S (2016) A survey of virtual machine placement techniques in a cloud data center. In: Proceedings of 1st International Conference on Information Security and Privacy, vol 78, pp 491–498Google Scholar
  13. 13.
    Schniederjans MJ, Cao Q, Triche JH (2013) Cloud computing. Part III, Chapter 12 in E-commerce operations management, vol 488, 2nd edn. World Scientific, Singapore, pp 301–327Google Scholar
  14. 14.
    Stegh Camati R, Calsavara A, Lima Jr L (2014) Solving the virtual machine placement problem as a multiple multidimensional knapsack problem. In: ICN 2014: The Thirteenth International Conference on Networks, pp 253–260Google Scholar
  15. 15.
    Li W, Tordsson J, Elmroth E (2011) Modeling for dynamic cloud scheduling via migration of virtual machines. In: 3rd IEEE International Conference on Cloud Computing Technology and Science, pp 163–171Google Scholar
  16. 16.
    Gu L, Zeng D, Guo S, Ye B (2015) Joint optimization of VM placement and request distribution for electricity cost cut in geo-distributed data centers. In: 2015 International Conference on Computing, Networking and Communications, Internet Services and Applications, pp 717–721Google Scholar
  17. 17.
    Khasnabish J, Mithani M, Rao S (2015) Tier-centric resource allocation in multi-tier cloud systems. IEEE Trans Cloud Comput 99:1–14Google Scholar
  18. 18.
    Bksi J, Galambos G, Kellerer H (2000) A 5/4 linear time bin packing algorithm. J Comput Syst Sci 60(1):145–160MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Jeyarani R, Nagaveni N, Ram RV (2012) Design and implementation of adaptive power-aware virtual machine provisioner (APA-VMP) using swarm intelligence. Future Gener Comput Syst 28:811–821CrossRefGoogle Scholar
  20. 20.
    Ajiro Y, Tanaka A (2007) Improving packing algorithms for server consolidation. In: Conference: 33rd International Computer Measurement Group ConferenceGoogle Scholar
  21. 21.
    Kalra M, Singh S (2015) A review of metaheuristic scheduling techniques in cloud computing. J Netw Comput Appl 16:275–295Google Scholar
  22. 22.
    Krishnaiyer K, Chena FF (2017) A cloud-based Kanban decision support system for resource scheduling and management. In: 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, Modena, pp 1489–1494Google Scholar
  23. 23.
    Fan X, Weber W, Barroso L (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th Annual International Symposium on Computer Architecture, pp 13–23Google Scholar
  24. 24.
    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. J Comput Syst Sci 79:1230–1242MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Kim N, Cho J, Seo E (2014) Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. J Future Gener Comput Syst 32:128–137CrossRefGoogle Scholar
  26. 26.
    Mazumdar S, Pranzo M (2017) Power efficient server consolidation for Cloud data center. Future Gener Comput Syst 70:4–16CrossRefGoogle Scholar
  27. 27.
    Lin W, Wang W, Wu W, Pang X, Liu B, Zhang Y (2017) A heuristic task scheduling algorithm based on server power efficiency model in cloud environments. In: Sustainable computing: informatics and systems. https://doi.org/10.1016/j.suscom.2017.10.007
  28. 28.
    Gupta MK, Amgoth T (2018) Resource-aware virtual machine placement algorithm for IaaS cloud. J Supercomput 74:122–140CrossRefGoogle Scholar
  29. 29.
    Huang Z, Tsang DH (2012) SLA guaranteed virtual machine consolidation for computing clouds. In: IEEE International Conference on Communications (ICC), pp 1314–1319Google Scholar
  30. 30.
    Huang Z, Tsang DH (2012) A virtual machine consolidation framework for mapreduce enabled computing clouds. In: Proceedings of the 24th International Teletraffic Congress. International Teletraffic Congress, pp 73–80Google Scholar
  31. 31.
    Dean J, Ghemawat S (2008) Mapreduce: simplified data processing on large clusters. Commun ACM 51:107–113CrossRefGoogle Scholar
  32. 32.
    Tawfeek MA, El-Sisi AB, Keshk AE, Torkey FA (2014) Virtual machine placement based on ant colony optimization for minimizing resource wastage. Adv Mach Learn Technol Appl 488:153–164Google Scholar
  33. 33.
    Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R (2010) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Published online 24, August 2010 in Wiley Online. Library 2011, vol 41, pp 23–50Google Scholar
  34. 34.
    Zheng Q, Li R, Li X, Shah N, Zhang J, Tian F, Chao K, Li J (2015) Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Gener Comput Syst 54:95–122CrossRefGoogle Scholar
  35. 35.
    Insight Into Cloud and Virtualization: Red Hat Survey Results from VMWorld. September 25, 2012. Retrieved from https://www.redhat.com/en/about/blog/insight-into-cloud-and-virtualization-red-hat-survey-results-from-vmworld
  36. 36.
    Chaisiri S, Lee B, Niyato D (2009) Optimal virtual machine placement across multiple cloud providers. In: Proceedings of the IEEE Asia-Pacific Services Computing Conference, pp 103–110Google Scholar
  37. 37.
    Bichler M, Setzer T, Speitkamp B (2006) Capacity planning for virtualized servers. In: Workshop on Information Technologies and Systems (WITS)Google Scholar
  38. 38.
    Speitkamp B, Bichler M (2010) A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans Serv Comput 3:266–278CrossRefGoogle Scholar
  39. 39.
    Mi H, Wang H, Yin G, Zhou Y, Shi D, Yuan L (2010) Online self-reconfiguration with performance guarantee for energy-efficient large-scale cloud computing data centers. In: Proceedings of the IEEE International Conference on Services Computing, pp 514–521Google Scholar
  40. 40.
    Xu J, Fortes J (2010) Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings of the IEEE/ACM International Conference on Green Computing and Communications and 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, pp 179–188Google Scholar
  41. 41.
    Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing SLA violations. In: Proceedings of the 10th IEEE Symposium on Integrated Management (IM), pp 119–128Google Scholar
  42. 42.
    Verma A, Ahuja P, Neogi A (2008) pMapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp 243–264Google Scholar
  43. 43.
    Srikantaiah S, Kansal A, Zhao F (2008) Energy aware consolidation for cloud computing. In: Proceedings of HotPower08 Workshop on Power Aware Computing and SystemsGoogle Scholar
  44. 44.
    Li B, Huai J, Wo T, Li Q, Zhong L (2009) Enacloud: an energy-saving application live placement approach for cloud computing environments. In: Proceedings of the IEEE International Conference on Cloud Computing, pp 17–24Google Scholar
  45. 45.
    Feller E, Rilling L, Morin C (2011) Energy-aware ant colony based workload placement in clouds. In: Proceedings of the IEEE/ACM International Conference on Grid Computing (GRID), pp 26–33Google Scholar
  46. 46.
    Zhang B, Qian Z, Huang W, Li X, Lu S (2012) Minimizing communication traffic in data centers with power-aware VM placement. In: 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous ComputingGoogle Scholar
  47. 47.
    Mosa A, Paton NW (2016) Optimizing virtual machine placement for energy and SLA in clouds using utility functions. J Cloud Comput 5:17CrossRefGoogle Scholar
  48. 48.
    Chuang Y, Chen C, Hwang C (2015) A real-coded genetic algorithm with a direction-based crossover operator. Inf Sci 305:320–348CrossRefGoogle Scholar
  49. 49.
    Maulik U, Bandyopadhyay S (2000) Genetic algorithm-based clustering technique. Pattern Recogn 33:1455–1465CrossRefGoogle Scholar
  50. 50.
    Zhu Q, Lin Q, Du Z, Liang Z, Wang W, Zhu Z, Chen J, Huang P, Ming Z (2016) A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm. Inf Sci 345:177–198CrossRefGoogle Scholar
  51. 51.
    Norman BA (2007) A new mutation operator for real coded genetic algorithms. Appl Math Comput 193:211–230MathSciNetzbMATHGoogle Scholar
  52. 52.
    Ghane-Kanafi A, Khorram E (2015) A new scalarization method for finding the efficient frontier in non-convex multi-objective problems. Appl Math Model 39:7483–7498MathSciNetCrossRefGoogle Scholar
  53. 53.
    Marler RT, Arora JS (2010) The weighted sum method for multi-objective optimization: new insights. Struct Multidiscip Optim 41:853–862MathSciNetCrossRefzbMATHGoogle Scholar
  54. 54.
    Ross SM (2013) Simulation. Chapter 4:47–68Google Scholar
  55. 55.
    Stefanello F, Buriol LS, Aggarwal V, Resende MGC (2015) A new linear model for placement of virtual machines across geo-separated data centers. Simpsio Bras Pesqui Oper 47:1–11Google Scholar
  56. 56.
    Canali C, Lancellotti R (2016) Scalable and automatic virtual machines placement based on behavioral similarities. Computing 99:1–21MathSciNetGoogle Scholar
  57. 57.
    Lodi A, Martello S, Vigo D (2002) Recent advances on two-dimensional bin packing problems. Discrete Appl Math 123:379–396MathSciNetCrossRefzbMATHGoogle Scholar
  58. 58.
    Patalia TP, Kulkarni GR (2010) Behavioral analysis of genetic algorithm for function optimization. In: IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp 1–5Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LARODEC, Institut Supérieur de GestionUniversité de TunisTunisTunisia

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