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A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing

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Abstract

In recent years, cloud data centers are rapidly growing with a large number of finite heterogeneous resources to meet the ever-growing user demands with respect to the SLA (service level agreement). However, the potential growth in the number of large-scale data centers leads to large amounts of energy consumption, which is constantly a major challenge. In addition to this challenge, intensive number of VM (virtual machine) migrations can decrease the performance of cloud data centers. Thus, how to minimize energy consumption while satisfying SLA and minimizing the number of VM migrations becomes an important challenge classified as NP-hard optimization problem in data centers. Most VM scheduling schemes have been proposed for this problem, such as dynamic VM consolidation. However, most of them failed in low time complexity and optimal solution. Hence, this paper proposes a dynamic VM consolidation approach-based load balancing to minimize the trade-off between energy consumption, SLA violations and VM migrations while keeping minimum host shutdowns and low time complexity in heterogeneous environment. Specifically, the proposed approach consists of four methods which include: BPSO meta-heuristic-based load balancing to impact on energy consumption and number of host shutdowns, overloading host detection and VM placement-based Pearson correlation coefficient to impact on SLA, and VM selection based on imbalance degree to impact on number of VM migration. Moreover, Pearson correlation coefficient and imbalance degree correlate CPU, RAM and bandwidth, respectively, in each host and each VM. Through extensive analysis and simulation experiments using real PlanetLab and random workloads, the performance results demonstrate that the proposed approach exhibits excellent results for the NP-problem.

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References

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

    Article  Google Scholar 

  2. Makaratzis AT, Giannoutakis KM, Tzovaras D (2018) Energy modeling in cloud simulation frameworks. Future Gener Comput Syst 79(2):715–725

    Article  Google Scholar 

  3. Khalil SA, Al-Haddad SAR, Hashim F, Abdullah ABHJ, Yussof S (2017) An effective approach for managing power consumption in cloud computing infrastructure. J Comput Sci 21:349–360

    Article  Google Scholar 

  4. Al-Dulaimy A, Itani W, Zantout R, Zekri A (2018) Type-aware virtual machine management for energy efficient cloud data centers. Sustain Comput Inform Syst 19:185–203

    Google Scholar 

  5. Fard SYZ, Ahmadi MR, Adabi S (2017) A dynamic VM consolidation technique for QoS and energy consumption in cloud environment. J Supercomput 73:4347–4368

    Article  Google Scholar 

  6. Mapetu JPB, Chen Z, Kong L (2019) Low-time complexity and low-cost binary particle swarm optimization algorithm for task scheduling and load balancing in cloud computing. Appl Intell 49(9):3308–3330

    Article  Google Scholar 

  7. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Abdullah M, Lu K, Wieder P, Yahyapour R (2017) A heuristic-based Approach for dynamic VMs consolidation in cloud data centers. Arab J Sci Eng 42(8):3535–3549

    Article  Google Scholar 

  11. Xu X, Zhang X, Khan M, Dou W, Xue S, Yu S A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems. Future Gener Comput Syst, In press, Available online (September 2017). http://dx.doi.org/10.1016/j.future.2017.08.057

  12. Zhou Z, Hu Z, Li K (2016) Virtual machine placement algorithm for both energy-awareness and SLA violation reduction in cloud data centers. Sci Program 1:11. https://doi.org/10.1155/2016/5612039

    Article  Google Scholar 

  13. Shrimali B, Patel H, Multi-objective optimization oriented policy for performance and energy efficient resource allocation in Cloud environment. J King Saud Univ Comput Inform Sci, In press, Available online (December 2017). https://doi.org/10.1016/j.jksuci.2017.12.001.

  14. Arianyan E, Taheri H, Sharifian S (2015) Novel energy and SLA efficient resource management heuristics for consolidation of virtual machines in cloud data centers. Comput Electr Eng 47:222–240

    Article  Google Scholar 

  15. He K, Li Z, Deng D, Chen Y (2017) Energy-efficient framework for virtual machine consolidation in cloud data centers. Netw Secur China Commun 14(10):192–201

    Article  Google Scholar 

  16. Minarolli D, Mazrekaj A, Freisleben B (2017) Tackling uncertainty in long-term predictions for host overload and underload detection in cloud computing. J Cloud Comput Adv Syst Appl 6(4):1–18

    Google Scholar 

  17. Bui DM, Yoonb Y, Huha EN, Jun S, Lee S (2017) Energy efficiency for cloud computing system based on predictive optimization. J Parallel Distrib Comput 102:103–114

    Article  Google Scholar 

  18. Melhem SB, Agarwal A, Goel N, Zaman M (2018) Markov prediction model for host load detection and VM placement in live migration. IEEE Access J 6:7190–7205

    Article  Google Scholar 

  19. Yadav R, Zhang W, Li K, Liu C, Shafiq M, Karn NK (2018) An adaptive heuristic for managing energy consumption and overloaded hosts in a cloud data center, Wireless Networks, In press. Available online. https://doi.org/10.1007/s11276-018-1874-1

    Article  Google Scholar 

  20. Maleklooa MH, Karaa N, Barachi ME (2018) An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments. Sustain Comput Inform Syst 17:9–24

    Google Scholar 

  21. Mosa A, Paton NW (2016) Optimizing virtual machine placement for energy and SLA in clouds using utility functions. J Cloud Comput Adv Syst Appl 5(17):1–17

    Google Scholar 

  22. Fu X, Zhao Q, Wang J, Zhang L, Qiao L (2018) Energy-aware vm initial placement strategy based on BPSO in cloud computing. Sci Program, Article ID 9471356

  23. Duan H, Chen C, Min G, Wu Y (2017) Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Future Gener Comput Syst 74:142–150

    Article  Google Scholar 

  24. Aryania A, Aghdasi HS, Khanli LM (2018) Energy-aware virtual machine consolidation algorithm based on ant colony system. J Grid Comput 16(3):477–491

    Article  Google Scholar 

  25. Kansal NJ, Chana I (2016) Energy-aware virtual machine migration for cloud computing-a firefly optimization approach. J Grid Comput 14(2):327–345

    Article  Google Scholar 

  26. Pascual JA, Botran TL, Alonso JM, Lozano JA (2015) Towards a greener cloud infrastructure management using optimized placement policies. J Grid Comput 13(3):375–389

    Article  Google Scholar 

  27. Feng L, Liao TW, Lin Z (2019) Two-level multi-task scheduling in a cloud manufacturing environment. Robot Comput Integr Manufact 56:127–139

    Article  Google Scholar 

  28. Weiwei L, Chen L, Wang JZ, Buyya R (2014) Bandwidth-aware divisible task scheduling for cloud computing. Softw Pract Exp 44:163–174

    Article  Google Scholar 

  29. Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer, Conference Proceedings Annual International Symposium on Computer Architecture, pp. 13–23, IEEE.

  30. Telenyk S, Zharikov E, Rolik O (2017) Consolidation of virtual machines using simulated annealing algorithm, Proceedings of the 2017 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 117–121, IEEE

  31. Rodriguez-Lujan I, Huerta R, Elkan C, Cruz CS (2010) Quadratic programming feature selection. J Mach Learn Res 11(2):1491–1516

    MathSciNet  MATH  Google Scholar 

  32. Xu J, Tang B, He H, Man H (2016) Semi supervised feature selection based on relevance and redundancy criteria. IEEE Trans Neural Netw Learn Syst 28(9):1974–1984

    Article  Google Scholar 

  33. Calheiros RN, Ranjan R, Beloglazov A, De-rose CAF, Buyya R (2011) CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. ACM Softw Pract Exp 41:23–50

    Article  Google Scholar 

  34. Humane P, Varshapriya JN (2015) Simulation of cloud infrastructure using CloudSim simulator: A practical approach for researchers. International conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Controls, Energy and Materials, pp. 207–211, IEEE.

  35. Park KS, Pai VS (2006) CoMon: a mostly-scalable monitoring system for PlanetLab. ACM SIGOPS Operat Syst Rev 40(1):47–65

    Article  Google Scholar 

  36. Ullah A, Li J, Shen Y, Hussain A (2018) A control theoretical view of cloud elasticity: taxonomy, survey and challenges. Clust Comput 21:1735–1764

    Article  Google Scholar 

  37. Beloglazov A, Buyya R (2015) OpenStack neat: a framework for dynamic and energy-efficient consolidation of virtual machines in Open-Stack clouds. Concurrency Comput Pract Exper 27(5):310–1333

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China (2017ZX05019001-011), the National Natural Science Foundation of China (61772450), the China Postdoctoral Science Foundation (2018M631764), Hebei Postdoctoral Research Program (B2018003009) and Doctoral Fund of Yanshan University (BL18003).

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Authors and Affiliations

  1. College of Information Science and Engineering, Yanshan University, Qinhuangdao, China

    Jean Pepe Buanga Mapetu, Lingfu Kong & Zhen Chen

  2. Faculty of Sciences, University of Kinshasa, Kinshasa, Congo

    Jean Pepe Buanga Mapetu

  3. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, 066004, China

    Jean Pepe Buanga Mapetu, Lingfu Kong & Zhen Chen

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  1. Jean Pepe Buanga Mapetu
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  2. Lingfu Kong
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Correspondence to Jean Pepe Buanga Mapetu.

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Mapetu, J.P.B., Kong, L. & Chen, Z. A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing. J Supercomput 77, 5840–5881 (2021). https://doi.org/10.1007/s11227-020-03494-6

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