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New comprehensive model based on virtual clusters and absorbing Markov chains for energy-efficient virtual machine management in cloud computing

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Abstract

Utilizing from energy-aware solutions along with maintaining service-level agreements is one of the most important research issues in cloud computing. In the proposed model, monitoring the status of resources and analysing the obtained data have led to proper placement and consolidation of virtual machines through targeted migrations at the right time. In the virtual machine placement policy, the definition of absorption mode has been used in simulated annealing algorithm in addition to the formation of virtual clusters to prevent from unlimited increase in the length of created Markov chain in any temperature while maintaining the convergence. The results of simulations obtained from various scenarios in CloudSim indicated the proposed model has led to energy savings up to 14.3%, 19% and 21% on low load, average load and high load, respectively, compared to the best understudy algorithm, while the SLA violation has also led to a decrease in all three modes.

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References

  1. Beloglazov A (2013) Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing. PhD Thesis, Melbourne University

  2. Ahmad F, Vijaykumar T (2010) Joint optimization of idle and cooling power in data centers while maintaining response time. ACM SIGPLAN Notices 45(3):243–256

    Google Scholar 

  3. Barroso LA, Holzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37

    Google Scholar 

  4. Quan DM, Mezza F, Sannenli D, Giafreda R (2013) T-Alloc: a practical energy efficient resource allocation algorithm for traditional data centers. Future Gener Comput Syst 28(5):791–800

    Google Scholar 

  5. Kumar MVR, Raghunathan S (2016) Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in Infrastructure clouds. J Comput Syst Sci 82(2):191–212

    MathSciNet  Google Scholar 

  6. Zhao DM, Zhou JT, Li K (2019) An Energy-Aware Algorithm for Virtual Machine Placement in Cloud Computing. IEEE. https://doi.org/10.1109/ACCESS.2019.2913175

  7. Rajabzadeh M, Haghighat AT (2017) Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers. J Supercomput 73(5):2001–2017

    Google Scholar 

  8. Salimian L, Esfahani FS, Shahraki MN (2016) An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines. Computing 98(6):641–660

    MathSciNet  Google Scholar 

  9. Nadjar A, Abrishami S, Deldari H (2017) Load dispersion-aware VM placement in favor of energy-performance tradeoff. J Supercomput 16(4):112–127

    Google Scholar 

  10. Su N, Shi A, Chen CH (2016) Research on virtual machine placement in the cloud based on improved simulated annealing algorithm. In: IEEE World Automation Congress (WAC), USA, pp 23–32

  11. Ferdaus MH, Murshed M, Calheiros RN, Buyya R (2017) An algorithm for network and data-aware placement of multi-tier applications in cloud data centers. J Netw Comput Appl 98(2):65–83

    Google Scholar 

  12. Kaur T, Chana I (2016) Energy aware scheduling of deadline-constrained tasks in cloud computing. Clust Comput 19(5):66–75

    Google Scholar 

  13. Aryania A, Aghdasi HS, Khanli LM (2018) Energy-aware virtual machine consolidation algorithm based on ant colony system. J Grid Comput. https://doi.org/10.1007/s10723-018-9428-4

    Article  Google Scholar 

  14. Mohiuddin I, Almogren A (2018) Workload aware VM consolidation method in edge/cloud computing for IoT applications. J Parallel Distrib Comput. https://doi.org/10.1016/j.jpdc.2018.09.011

    Article  Google Scholar 

  15. Heyang X, Yang L, Wei W, Ying X (2019) Migration cost and energy-aware virtual machine consolidation under cloud environments considering remaining runtime. Int J Parallel Program. https://doi.org/10.1007/s10766-018-00622-x

    Article  Google Scholar 

  16. Zhihua L, Chengyu Y, Lei Y, Xinrong Y (2019) Energy-aware and multi-resource overload probability constraint-based virtual machine dynamic consolidation method. Future Gener Comput Syst 80(3):139–156

    Google Scholar 

  17. 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 7(8):1230–1242

    MathSciNet  MATH  Google Scholar 

  18. Kliazovich D, Bouvry P, Khan SU (2013) DENS: data center energy efficient network-aware scheduling. Clust Comput 16(1):65–75

    Google Scholar 

  19. Deng W, Liu F, Jin H, Liao X, Liu H (2014) Reliability-aware server consolidation for balancing energy-lifetime tradeoff in virtualized cloud datacenters. Int J Commun Syst 27(4):623–642

    Google Scholar 

  20. Garg SK, Toosi AN, Gopalaiyengar SK, Buyya R (2014) SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter. J Netw Comput Appl 45(6):108–120

    Google Scholar 

  21. Song W, Xiao Z, Chen Q, Luo H (2015) Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans Comput 63(11):2647–2660

    MathSciNet  MATH  Google Scholar 

  22. Rethinagiri SK, Palomar O, Sobe A, Yalcin G, Knauth T, Gil RT, Prieto P, Schneega M, Cristal A, Unsal O (2016) ParaDIME: parallel distributed infrastructure for minimization of energy for data centers. Microprocess Microsyst 39(8):1174–1189

    Google Scholar 

  23. Dong J, Wang H, Cheng S (2015) Energy-performance tradeoffs in IaaS cloud with virtual machine scheduling. Communications 12(2):155–166

    Google Scholar 

  24. Carli T, Henriot S, Cohen J, Tomasik J (2017) A packing problem approach to energy-aware load distribution in clouds. Sustain Comput Inform Syst 9(2):20–32

    Google Scholar 

  25. Lin W, Xu S, Li J, Xu L, Peng Z (2017) Design and theoretical analysis of virtual machine placement algorithm based on peak workload characteristics. Soft Comput 21(5):1301–1314

    MATH  Google Scholar 

  26. Zhang R, Zhong AM, Dong B, Tian F, Li R (2019) Container-VM-PM architecture: a novel architecture for docker container placement. In: International Conference on Cloud Computing. Springer International Publishing, Cham

  27. Dhingra A, Paul S (2014) Green cloud: heuristic based BFO technique to optimize resource allocation. Indian J Sci Technol 7(5):685–691

    Google Scholar 

  28. Park KS, Pai SV (2006) CoMon: a mostly-scalable monitoring system for planet-lab. ACM SIGOPS Oper Syst Rev 40:65–74

    Google Scholar 

  29. 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 13(1):1397–1420

    Google Scholar 

  30. Ferdaus MH (2016) Multi-objective Virtual Machine Management in Cloud Data Centers. PhD Thesis, Monash University

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Acknowledgements

This work sponsored by Islamic Azad University Science and Research Branch.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mehdi Rajabzadeh & Amir Masoud Rahmani

  2. Department of Computer Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Abolfazl Toroghi Haghighat

Authors
  1. Mehdi Rajabzadeh
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  2. Abolfazl Toroghi Haghighat
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  3. Amir Masoud Rahmani
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Correspondence to Abolfazl Toroghi Haghighat.

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Rajabzadeh, M., Toroghi Haghighat, A. & Rahmani, A.M. New comprehensive model based on virtual clusters and absorbing Markov chains for energy-efficient virtual machine management in cloud computing. J Supercomput 76, 7438–7457 (2020). https://doi.org/10.1007/s11227-020-03169-2

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