Skip to main content
Log in

Optimal VM placement in distributed cloud environment using MOEA/D

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Virtual machine placement is the concept of hosting the virtual machines to appropriate physical servers so as to meet user computation requirements. An optimal placement is one of the key concerns in green cloud computing. Virtual machine placement in distributed cloud environment also imposes propagation time as a key for effective hosting of VM along with CPU and memory resource constraints. In this paper, MOEA/D a multi-objective evolutionary algorithm is used to find a non-dominated solution w.r.t. minimal wastage, minimal power consumption and less propagation delay. The proposed algorithm has been implemented, tested and compared with the existing multi-objective approaches. The statistical analysis of the simulation results proves that MOEA/D outperforms against the existing algorithms in distributed cloud VM placement.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ahmadi MH, Ahmadi MA (2015a) Thermodynamic analysis and optimization of an irreversible radiative type heat engine by using non-dominated sorting genetic algorithm. Int J Ambient Energy 37:403–408

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA (2015b) Thermodynamic analysis and optimization of an irreversible Ericsson cryogenic refrigerator cycle. Energy Convers Manag 89C:147–155

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Mehrpooya M, Hosseinzade H, Feidt M (2014a) Thermodynamic and thermo-economic analysis and optimization of performance of irreversible four-temperature-level absorption refrigeration. Energy Converg Manag 88C:1051–1059

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Mohammadi AH, Feidt M, Pourkiaei SM (2014b) Multi-objective optimization of an irreversible Stirling cryogenic refrigerator cycle. Energy Convers Manag 82:351–360

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Mohammadi AH, Mehrpooya M, Feidt M (2014c) Thermodynamic optimization of Stirling heat pump based on multiple. Energy Convers Manag 80:319–328

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Sadatsakkak SA (2015a) Thermodynamic analysis and performance optimization of irreversible Carnot refrigerator by using multi objective evolutionary algorithms (MOEAs). Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2015.07.006

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Shafaei A, Ashouri M, Toghyani S (2015b) Thermodynamic analysis and optimization of the Atkinson engine by using NSGA-II. Int J Low Carbon Technol 11:317–324

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Bayat R, Ashouri M, Feidt M (2015c) Thermo-economic optimization of Stirling heat pump by using non-dominated sorting genetic algorithm. Energy Convers Manag 91:315–322

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Mehrpooya M, Sameti M (2015d) Thermo-ecological analysis and optimization performance of an irreversible three-heat-source absorption heat pump. Energy Convers Manag. https://doi.org/10.1016/j.enconman.2014.11.021

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Feidt M (2015e) Thermodynamic analysis and evolutionary algorithm based on multi-objective optimization of performance of irreversible four-temperature-level absorption refrigeration. Mech Ind 16:207

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Mehrpooya M, Pourkiaei SM, Khalili M (2015f) Thermodynamic analysis and evolutionary algorithm based on multi-objective optimization of Rankine cycle heat engine. Int J Ambient Energy 37:363–371

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Mehrpooya M (2016a) Investigation of design parameters effect on power output and thermal efficiency of the Stirling engine thermodynamic analysis. Int J Low Carbon Technol. https://doi.org/10.1093/ijlct/ctu030

    Article  Google Scholar 

  • Ahmadi MH, Ahmadi MA, Feidt M (2016b) Performance optimization of a solar-driven multi-step irreversible Brayton cycle based on a multi-objective genetic algorithm. Oil Gas Sci Technol 1:1–10. https://doi.org/10.2516/ogst/2014028

    Article  Google Scholar 

  • Alahmadi A, Alnowiser A, Zhu MM, Che D, Ghodous P (2014) Enhanced first-fit decreasing algorithm for energy-aware job scheduling in cloud. In: 2014 international conference on computational science and computational intelligence (CSCI), vol 2. IEEE, pp 69–74

  • Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing. IEEE Computer Society, Washington

  • Beloglazov A, Buyya R (2013) Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans Parallel Distrib Syst 24(7):1366–1379

    Article  Google Scholar 

  • Bobroff N, Kochut A, Beaty K (2007) Dynamic placement of virtual machines for managing sla violations. In: 10th IFIP/IEEE international symposium on integrated network management, 2007. IM’07. IEEE

  • Buyya R, Beloglazov A, Abawajy J (2010) Energy-efficient management of data center resources for cloud computing: a vision, architectural elements, and open challenges. arXiv preprint arXiv:1006.0308

  • Chen M, Zhang H, Su YY, Wang X, Jiang G, Yoshihira K (2011) Effective VM sizing in virtualized data centers. In: 2011 IFIP/IEEE international symposium on integrated network management (IM). IEEE, pp 594–601

  • Chowdhury MR, Mahmud MR, Rahman RM (2015) Study and performance analysis of various VM placement strategies. In: 2015 16th IEEE/ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD). IEEE

  • Coello CAC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, New York

    MATH  Google Scholar 

  • Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York

    MATH  Google Scholar 

  • Dupont C, Schulze T, Giuliani G, Somov A, Hermenier F (2012) An energy aware framework for virtual machine placement in cloud federated data centres. In: 2012 third international conference on future energy systems: where energy, computing and communication meet (e-energy). IEEE, pp 1–10

  • Fan X, Weber W-D, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: ACM SIGARCH computer architecture news, vol 35, no 2. ACM, New York

  • 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 

  • Ghribi C, Hadji M, Zeghlache D (2013) Energy efficient vm scheduling for cloud data centers: exact allocation and migration algorithms. In: 2013 13th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGrid). IEEE

  • Goldberg DE, Lingle R (1985) Alleles, loci, and the traveling salesman problem. In: Proceedings of an international conference on genetic algorithms and their applications, vol 154. Lawrence Erlbaum, Hillsdale

  • Jiankang D, Hongbo W, Shiduan C (2015) Energy-performance tradeoffs in IaaS cloud with virtual machine scheduling. China Commun 12(2):155–166

    Article  Google Scholar 

  • Miettinen K (2012) Nonlinear multiobjective optimization, vol 12. Springer, Berlin

    MATH  Google Scholar 

  • Mishra M, Sahoo A (2011) On theory of vm placement: anomalies in existing methodologies and their mitigation using a novel vector based approach. In: 2011 IEEE international conference on cloud computing (CLOUD). IEEE

  • Sadatsakkak SA, Ahmadi MH, Ahmadi MA (2015a) Optimization performance and thermodynamic analysis of an irreversible nano scale Brayton cycle operating with Maxwell–Boltzmann gas. Energy Convers Manag 101:592–605

    Article  Google Scholar 

  • Sadatsakkak SA, Ahmadi MH, Ahmadi MA (2015b) Thermodynamic and thermo-economic analysis and optimization of an irreversible regenerative closed Brayton cycle. Energy Convers Manag 94:124–129

    Article  Google Scholar 

  • Schott JR (1995) Fault tolerant design using single and multicriteria genetic algorithm optimization. No. AFIT/CI/CIA-95-039. Air Force Inst of Tech Wright–Patterson AFB OH

  • Singh A, Korupolu M, Mohapatra D (2008) Server-storage virtualization: integration and load balancing in data centers. In: Proceedings of the 2008 ACM/IEEE conference on supercomputing. IEEE Press

  • Song W, Xiao Z, Chen Q, Luo H (2014) Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans Comput 63(11):2647–2660

    Article  MathSciNet  Google Scholar 

  • Speitkamp B, Bichler M (2010) A mathematical programming approach for server consolidation problems in virtualized data centers. IEEE Trans Serv Comput 3(4):266–278

    Article  Google Scholar 

  • Tang C, Steinder M, Spreitzer M, Pacifici G (2007) A scalable application placement controller for enterprise data centers. In: Proceedings of the 16th international conference on World Wide Web. ACM, New York, pp 331–340

  • Van Veldhuizen DA (1999) Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. No. AFIT/DS/ENG/99-01. Air Force Inst of Tech Wright–Patterson AFB OH School of Engineering

  • Wang M, Meng X, Zhang L (2011) Consolidating virtual machines with dynamic bandwidth demand in data centers. In: INFOCOM, 2011 proceedings IEEE. IEEE

  • Wood T, Shenoy P, Venkataramani A, Yousif M (2009) Sandpiper: black-box and gray-box resource management for virtual machines. Comput Netw 53(17):2923–2938

    Article  Google Scholar 

  • Zhang Q, Li H (2007) MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  • Zhang L, Zhuang Y, Zhu W (2013) Constraint programming based virtual cloud resources allocation model. Int J Hybrid Inf Technol 6(6):333–344

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arunkumar Gopu.

Ethics declarations

Conflict of interest

The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gopu, A., Venkataraman, N. Optimal VM placement in distributed cloud environment using MOEA/D. Soft Comput 23, 11277–11296 (2019). https://doi.org/10.1007/s00500-018-03686-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-018-03686-6

Keywords

Navigation