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Discrete PSO-based workload optimization in virtual machine placement

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Abstract

Virtual machine placement has great potential to significantly improve the efficiency of resource utilization in a cloud center. Focusing on CPU and memory resource, this paper presents SOWO—a discrete particle swarm optimization-based workload optimization approach to minimize the number of active physical machines in virtual machine placement. The experiment results show the usability and superiority of SOWO. Compared with the OpenStack native scheduler, SOWO decreases the physical machine consumption by at least 50% and increases the memory utilization of physical machine by more than two times.

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References

  1. Vaquero LM, Rodero-Merino L, Caceres J, Lindner M (2009) A break in the clouds: towards a cloud definition. ACM SIGCOMM Comput Commun Rev 39:50–55

    Article  Google Scholar 

  2. Grandison T, Maximilien EM, Thorpe S, Alba A (2010) Towards a formal definition of a computing cloud. In: Proceedings of the 2010 6th World Congress on Services. IEEE Computer Society, Washington, DC, pp 191–192

  3. Rimal BP, Choi E (2012) A service-oriented taxonomical spectrum, cloudy challenges and opportunities of cloud computing. Int J Commun Syst 25(6):796–819. https://doi.org/10.1002/dac.1279

    Article  Google Scholar 

  4. Coffman EG Jr., Garey MR, Johnson DS (1997) Approximation algorithms for bin packing: a survey. In: Hochbaum DS (ed) Approximation algorithms for NP-hard problems. PWS Publishing Co., Boston, p 46–93

  5. OpenStack. http://docs.openstack.org/. Accessed March 2017

  6. Verma A, Ahuja P, Neog 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. Springer-Verlag New York, Inc., New York, pp 243–264

  7. Xu J, Fortes JAB (2010) Multi-objective virtual machine placement in virtualized data center environments. In: Proceedings 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010 179–188. https://doi.org/10.1109/GreenCom-CPSCom.2010.137

  8. Srikantaiah S, Kansal A, Zhao F (2011) Singular value decomposition for the truncated Hilbert transform: part II. Proc Power Aware Comput Syst 27(7):929–931

    MathSciNet  Google Scholar 

  9. R. Nathuji, K. Schwan, Virtual power: coordinated power management in virtualized enterprise systems, ACM SIGOPS Oper Syst Rev, vo.l41, no.6, pp.265–278, 2007

  10. Kusic D, Kephart JO, Hanson JE, Kandasamy N, Jiang G (2009) Power and performance management of virtualized computing environments via look ahead control. Clust Comput 12(1):1–15. https://doi.org/10.1007/s10586-008-0070-y

    Article  Google Scholar 

  11. Rao KS, Thilagam PS (2015) Heuristics based server consolidation with residual resource defragmentation in cloud data centers. Futur Gener Comput Syst 50:87–98

    Article  Google Scholar 

  12. Viswanathan H, Lee EK, Rodero I, Pompili D, Parashar M, Gamell M (2011) Energy-aware application-centric VM allocation for HPC workloads. In: Proceedings of the 2011 I.E. International Symposium on Parallel and Distributed Processing Workshops and PhD Forum. IEEE Computer Society, Washington, DC, pp 890–897

  13. Sharkh MA, Ouda A, Shami A (2013) A resource scheduling model for cloud computing data centers. In: 2013 9th International Wireless Communications and Mobile Computing Conference. pp 213–218. https://doi.org/10.1109/IWCMC.2013.6583561

  14. von Laszewski G, Diaz J, Wang F, Fox GC 2012 Comparison of multiple cloud frameworks. In: Proceedings of the 2012 I.E. Fifth International Conference on Cloud Computing. IEEE Computer Society, Washington, DC, pp 734–741

  15. Kyi HM, Naing TT (2011) An efficient approach for virtual machines scheduling on a private cloud environment. In: 2011 4th IEEE International Conference on Broadband Network and Multimedia Technology. pp 365–369. https://doi.org/10.1109/ICBNMT.2011.6155958

  16. Mishra M, Sahoo A (2011) On theory of VM placement: anomalies in existing methodologies and their mitigation using a novel vector based approach. In: Proceedings of the 2011 I.E. 4th International Conference on Cloud Computing. IEEE Computer Society, Washington, DC, pp 275–282

  17. Qingling W, Varela CA (2011) Impact of cloud computing virtualization strategies on workloads’ performance. In: Proceedings of the 2011 Fourth IEEE International Conference on Utility and Cloud Computing. IEEE Computer Society, Washington, DC, pp 130–137

  18. Sijin H, Li G, Ghanem M, Yike G (2012) Improving resource utilization in the cloud environment using multivariate probabilistic models. In: Proceedings of the 2012 I.E. Fifth International Conference on Cloud Computing. IEEE Computer Society, Washington, DC, pp 574–581

  19. Do AV, Chen J, Wang C, Lee YC, Zomaya AY, Zhou BB (2011) Profiling applications for virtual machine placement in clouds. In: Proceedings of the 2011 I.E. 4th International Conference on Cloud Computing. IEEE Computer Society, Washington, DC, pp 660–667

  20. Verboven S, Vanmechelen K, Broeckhove J (2015) Network aware scheduling for virtual machine workloads with interference models. IEEE Trans Serv Comput 8(4):617–629

    Article  Google Scholar 

  21. Zhang Q, Zhani MF, Zhang S,Zhu Q, Boutaba R, Hellerstein JL (2012) Dynamic energy-aware capacity provisioning for cloud computing environment. In: Proceedings of the 9th International Conference on Autonomic Computing. ACM, New York, pp 145–154

  22. Ferreto TC, Netto MAS, Calheiros RN, Rose CAFD (2011) Server consolidation with migration control for virtualized data centers. Futur Gener Comput Syst 27(8):1027–1034. https://doi.org/10.1016/j.future.2011.04.016

    Article  Google Scholar 

  23. Jiang JW, Lan T, Ha S, Chen M, Chiang M (2012) Joint VM placement and routing for data center traffic engineering. IEEE Inf Proc 131(5):2876–2880

    Google Scholar 

  24. J. Zhang, X. Wang, H. Huang H, and S. Chen, Clustering based virtual machines placement in distributed cloud computing, Futur Gener Comput Syst, vol.66, pp.1–10, 2017, DOI: https://doi.org/10.1016/j.future.2016.06.018

  25. Wen X, Gu G, Li Q, Gao Y, Zhang X (2012) Comparison of open-source cloud management platforms: OpenStack and OpenNebula. In: 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery. pp 2457–2461. https://doi.org/10.1109/FSKD.2012.6234218

  26. Litvinski O, Gherbi A (2013) Experimental evaluation of OpenStack Compute Scheduler. Procedia Comput Sci 19:116–123. https://doi.org/10.1016/j.procs.2013.06.020

    Article  Google Scholar 

  27. Sahasrabudhe S, Sonawani SS (2015) Improved filter-weight algorithm for utilization-aware resource scheduling in OpenStack. In: 2015 International Conference on Information Processing (ICIP). pp 43–47. https://doi.org/10.1109/INFOP.2015.7489348

  28. Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948

    Article  Google Scholar 

  29. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73. https://doi.org/10.1109/4235.985692

    Article  Google Scholar 

  30. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. IEEE Proc Syst Man Cybern Comput Cybern Simul 5:4104–4108

    Google Scholar 

  31. Wood T, Shenoy PJ, Yousif M, Yousif M (2009) Sandpiper: black-box and gray-box resource management for virtual machines. Comput Netw 53(17):2923–2938. https://doi.org/10.1016/j.comnet.2009.04.014

    Article  MATH  Google Scholar 

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Funding

This work is supported by the National Science and Technology Major Project under grant no. 2017YFB0803001, the National Natural Science Foundation of China 61370215, 61370211, and the Open Project Foundation of Information Security Evaluation Center of Civil Aviation, Civil Aviation University of China no. CAAC-ISECCA-201703.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Jianen Yan, Hongli Zhang, Haiyan Xu & Zhaoxin Zhang

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  1. Jianen Yan
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  2. Hongli Zhang
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  3. Haiyan Xu
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  4. Zhaoxin Zhang
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Correspondence to Haiyan Xu.

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Yan, J., Zhang, H., Xu, H. et al. Discrete PSO-based workload optimization in virtual machine placement. Pers Ubiquit Comput 22, 589–596 (2018). https://doi.org/10.1007/s00779-018-1111-z

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  • DOI: https://doi.org/10.1007/s00779-018-1111-z

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