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Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation

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Abstract

The virtual machine (VM) workload of a datacenter is dynamic, where the reallocation of a subset of active VMs can result in better VM allocation by avoiding over-loaded/under-loaded physical machines (PMs). Over-loaded PMs lead to customer dissatisfaction, whereas under-loaded PMs result in increased energy consumption. In this work, we propose a multi-objective best-fit-decreasing (BFD) approach to the VM reallocation problem. Our multi-objective formulation considers power costs and resource utilization. We use the expressive power of fuzzy algebra to combine both objectives into a single-objective function. Extensive simulations, using CloudSim, show that our fuzzy-based multi-objective implementation of BFD leads to significantly better solutions with respect to energy and resource utilization. Indeed, the results show an improvement of as much as 30% to 40% of energy consumption and 30% of resource utilization when compared with reported heuristics which minimize energy only, using five real workloads provided as a part of the coMon project, which is a monitoring infrastructure for PlanetLab.

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Notes

  1. http://www.spec.org/power_ssj2008/.

  2. http://aws.amazon.com/ec2/instance-types/.

References

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

    Article  Google Scholar 

  2. https://www.citrix.com/products/citrix-hypervisor/

  3. https://www.vmware.com/products/esxi-and-esx.html

  4. https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/

  5. Braiki K, Youssef H (2019) Resource management in cloud data centers: a survey. In: 2019 15th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE 2019, pp 1007–1012

  6. Chekuri C, Khanna S (1999) On multi-dimensional packing problems. In: Proceedings of the Tenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, pp 185–194

  7. Youssef H, Sait SM (2003) Iterative computer algorithms with applications in engineering-chapter 2: Partitioning

  8. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  9. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gen Comput Syst 28(5):755–768

    Article  Google Scholar 

  10. Zeng D, Guo S, Huang H, Yu S, Leung VC (2015) Optimal VM placement in data centres with architectural and resource constraints. Int J Auton Adapt Commun Syst 8(4):392–406

    Article  Google Scholar 

  11. Sun H, Stolf P, Pierson J-M, Da Costa G (2014) Energy-efficient and thermal-aware resource management for heterogeneous datacenters. Sustain Comput Inf Syst 4(4):292–306

    Google Scholar 

  12. Gupta MK, Amgoth T (2018) Resource-aware virtual machine placement algorithm for IaaS cloud. J Supercomput 74(1):122–140

    Article  Google Scholar 

  13. Abdel-Basset M, Abdle-Fatah L, Sangaiah AK (2018) An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment. Cluster Comput 22:1–16

    Google Scholar 

  14. Alharbi F, Tian Y-C, Tang M, Zhang W-Z, Peng C, Fei M (2019) An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Syst Appl 120:228–238

    Article  Google Scholar 

  15. Sharma N, Guddeti RM (2016) Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans Serv Comput 12:158–171

    Article  Google Scholar 

  16. Riahi M, Krichen S (2018) A multi-objective decision support framework for virtual machine placement in cloud data centers: a real case study. J Supercomput 74(7):2984–3015

    Article  Google Scholar 

  17. Chen X, Chen Y, Zomaya AY, Ranjan R, Hu S (2016) CEVP: cross entropy based virtual machine placement for energy optimization in clouds. J Supercomput 72(8):3194–3209

    Article  Google Scholar 

  18. Zhao H, Zheng Q, Zhang W, Chen Y, Huang Y (2015) Virtual machine placement based on the vm performance models in cloud. In: Computing and Communications Conference (IPCCC), 2015 IEEE 34th International Performance. IEEE 2015, pp 1–8

  19. Tang M, Pan S (2015) A hybrid genetic algorithm for the energy-efficient virtual machine placement problem in data centers. Neural Process Lett 41(2):211–221

    Article  Google Scholar 

  20. Xu J, Fortes J (2011) A multi-objective approach to virtual machine management in datacenters. In: Proceedings of the 8th ACM International Conference on Autonomic Computing. ACM, pp 225–234

  21. Antonescu A-F, Robinson P, Braun T (2013) Dynamic SLA management with forecasting using multi-objective optimization. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM, 2013). IEEE, pp 457–463

  22. Horri A, Mozafari MS, Dastghaibyfard G (2014) Novel resource allocation algorithms to performance and energy efficiency in cloud computing. J Supercomput 69(3):1445–1461

    Article  Google Scholar 

  23. Dong D, Herbert J (2013) Energy efficient VM placement supported by data analytic service. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE, pp 648–655

  24. Dashti SE, Rahmani AM (2016) Dynamic VMS placement for energy efficiency by PSO in cloud computing. J Exp Theor Artif Intell 28(1–2):97–112

    Article  Google Scholar 

  25. Braiki K, Youssef H (2018) Multi-objective virtual machine placement algorithm based on particle swarm optimization. In: 14th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE, pp 279–284

  26. Nandi BB, Banerjee A, Ghosh SC, Banerjee N (2012) Stochastic vm multiplexing for datacenter consolidation. In: 2012 IEEE Ninth International Conference on Services Computing (SCC). IEEE, pp 114–121

  27. Sun M, Gu W, Zhang X, Shi H, Zhang W (2013) A matrix transformation algorithm for virtual machine placement in cloud. In: 2013 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). IEEE, pp 1778–1783

  28. Duong-Ba T, Nguyen T, Bose B, Tran T (2014) Joint virtual machine placement and migration scheme for datacenters. In: Global Communications Conference (GLOBECOM), 2014 IEEE. IEEE, pp 2320–2325

  29. Zhang J, He Z, Huang H, Wang X, Gu C, Zhang L (2014) Sla aware cost efficient virtual machines placement in cloud computing. In: Performance Computing and Communications Conference (IPCCC), 2014 IEEE International. IEEE, pp 1–8

  30. Guérout T, Gaoua Y, Artigues C, Da Costa G, Lopez P, Monteil T (2017) Mixed integer linear programming for quality of service optimization in clouds. Future Gen Comput Syst 71:1–17

    Article  Google Scholar 

  31. Zhao H, Wang J, Liu F, Wang Q, Zhang W, Zheng Q (2018) Power-aware and performance-guaranteed virtual machine placement in the cloud. IEEE Trans Paral Distrib Syst 29(6):1385–1400

    Article  Google Scholar 

  32. Duong-Ba TH, Nguyen T, Bose B, Tran TT (2018) A dynamic virtual machine placement and migration scheme for data centers. IEEE Transactions on Services Computing, IEEE

  33. 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(8):1230–1242

    Article  MathSciNet  Google Scholar 

  34. Zheng Q, Li R, Li X, Shah N, Zhang J, Tian F, Chao K-M, Li J (2016) Virtual machine consolidated placement based on multi-objective biogeography-based optimization. Future Gen Comput Syst 54:95–122

    Article  Google Scholar 

  35. Pires FL, Barán B (2013) Multi-objective virtual machine placement with service level agreement: a memetic algorithm approach. In: Proceedings of the 2013 IEEE/ACM 6th International Conference on Utility and Cloud Computing. IEEE Computer Society, pp 203–210

  36. López-Pires F, Barán B (2017) Many-objective virtual machine placement. J Grid Comput 15(2):161–176

    Article  Google Scholar 

  37. Huang D, Yang D, Zhang H, Wu L (2012) Energy-aware virtual machine placement in data centers. In: Global Communications Conference (GLOBECOM), 2012 IEEE. IEEE, pp 3243–3249

  38. Arianyan E, Taheri H, Khoshdel V (2017) Novel fuzzy multi objective DVFS-aware consolidation heuristics for energy and SLA efficient resource management in cloud data centers. J Netw Comput Appl 78:43–61

    Article  Google Scholar 

  39. Xu J, Fortes JA (2010) Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing. IEEE Computer Society, pp 179–188

  40. Sait SM, Bala A, El-Maleh AH (2016) Cuckoo search based resource optimization of datacenters. Appl Intell 44(3):489–506

    Article  Google Scholar 

  41. Ramezani F, Naderpour M, Lu J (2016) A multi-objective optimization model for virtual machine mapping in cloud data centres. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, pp 1259–1265

  42. Chaabouni T, Khemakhem M (2018) Energy management strategy in cloud computing: a perspective study. J Supercomput 74(12):6569–6597

    Article  Google Scholar 

  43. Flener P, Frisch A, Hnich B, Kiziltan Z, Miguel I, Walsh T (2001) Matrix modelling. In: Proceedings of the CP-01 Workshop on Modelling and Problem Formulation, p 223

  44. Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1:28–44

    Article  MathSciNet  Google Scholar 

  45. Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MathSciNet  Google Scholar 

  46. Yager RR, Filev DP (1994) Parameterized and-uke and or-like OWA operators. Int J Gen Syst 22(3):297–316

    Article  Google Scholar 

  47. Calheiros RN, Ranjan R, Beloglazov A, De Rose CA, Buyya R (2011) Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw Pract Exp 41(1):23–50

    Article  Google Scholar 

  48. 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 

  49. Park K, Pai VS (2006) Comon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Oper Syst Rev 40(1):65–74

    Article  Google Scholar 

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Authors and Affiliations

  1. PRINCE Research Laboratory, University of Sousse, ISITCom Hammam Sousse, Sousse, Tunisia

    Khaoula Braiki & Habib Youssef

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  1. Khaoula Braiki
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  2. Habib Youssef
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Correspondence to Khaoula Braiki.

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Braiki, K., Youssef, H. Fuzzy-logic-based multi-objective best-fit-decreasing virtual machine reallocation. J Supercomput 76, 427–454 (2020). https://doi.org/10.1007/s11227-019-03029-8

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