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Efficient resource allocation and management by using load balanced multi-dimensional bin packing heuristic in cloud data centers

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Abstract

Resource optimization is becoming a prime factor in the progress of Internet-based technology, Cloud Computing. A resource management model is highly required in cloud data center paradigms to utilize available resources effectively. Bin-Packing problem is an applicable combinatorial optimization for Virtual Machine (VM) to Physical Machine (PM) allocation to minimize the required PMs. In this paper, we have proposed an efficient resource allocation and management algorithm in two phases. During the first phase, a Load Balanced Multi-Dimensional Bin-Packing (LBMBP) heuristic for Virtual Machine (VM) to Physical Machine (PMs or host) allocation is introduced, considering multidimensional resources: CPU, RAM, and Network Bandwidth. In the Second Phase, to perform VM migration, a mechanism to detect overloaded and underloaded hosts based on outliers has been described. The proposed work illustrated the simulation results using CloudSim Plus Simulator and observed a reduction in the number of active PMs. Energy consumption and the number of migrations with improved resource utilization.

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References

  1. Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Fut Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  2. Hu F, Qiu M, Li J, Grant T, Taylor D, McCaleb S, Butler L, Hamner R (2011) A review on cloud computing: design challenges in architecture and security. J Comput Inf Technol 19(1):25–55

    Article  Google Scholar 

  3. Madni SH, Abd Latiff MS, Coulibaly Y (2016) Resource scheduling for infrastructure as a service (IaaS) in cloud computing: challenges and opportunities. J Netw Comput Appl 68:173–200

    Article  Google Scholar 

  4. Pettey C, Goasduff L (2017) Gartner says worldwide public cloud services market to grow 18 percent in 2017. Press Release, Gartner

    Google Scholar 

  5. Zhang J, Huang H, Wang X (2016) Resource provision algorithms in cloud computing: a survey. J Netw Comput Appl 64:23–42

    Article  Google Scholar 

  6. Ahmad RW, Gani A, Hamid SH, Shiraz M, Yousafzai A, Xia F (2015) A survey on virtual machine migration and server consolidation frameworks for cloud data centers. J Netw Comput Appl 52:11–25

    Article  Google Scholar 

  7. Ranganathan P (2010) Recipe for efficiency: principles of power-aware computing. Commun ACM 53(4):60–67

    Article  Google Scholar 

  8. Nehra P, Nagaraju A (2017) Scheduling for resource utilization and load balancing in cloud environment. In: 4th international conference on computing for sustainable global development, 2017 (Accepted)

  9. Mahrishi M, Nagaraju A (2012) Optimizing cloud service provider scheduling by using rough set model. In: 2012 international conference on cloud computing technologies, applications and management (ICCCTAM) pp 223-228

  10. Zhu W, Zhuang Y, Zhang L (2017) A three-dimensional virtual resource scheduling method for energy saving in cloud computing. Futur Gener Comput Syst 69:66–74

    Article  Google Scholar 

  11. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Futur Gener Comput Syst 28(5):755–68

    Article  Google Scholar 

  12. Jin H, Pan D, Xu J, Pissinou N (2012) Efficient VM placement with multiple deterministic and stochastic resources in data centers. In 2012 IEEE global communications conference (GLOBECOM) pp 2505–2510

  13. Shi L, Butler B, Botvich D, Jennings B (2013) Provisioning of requests for virtual machine sets with placement constraints in IaaS clouds. In: 2013 IFIP/IEEE international symposium on integrated network management, pp 499–505

  14. Beloglazov A, Buyya R (2010) Energy efficient allocation of virtual machines in cloud data centers. In: 2010 10th IEEE/ACM international conference on cluster, cloud and grid computing, pp 577–578

  15. Beloglazov A, Buyya R (2010) Adaptive threshold-based approach for energy-efficient consolidation of virtual machines in cloud data centers. MGC@ Middleware. pp 799–803

  16. Li Z, Yan C, Yu X, Yu N (2017) Bayesian network-based virtual machines consolidation method. Fut Gener Comput Syst, pp 75–87

  17. Sharma O, Saini H (2016) Vm consolidation for cloud data center using median based threshold approach. Proc Comput Sci 89:27–33

    Article  Google Scholar 

  18. Trivella A, Pisinger D (2016) The load-balanced multi-dimensional bin-packing problem. Comput Oper Res 74:152–64

    Article  MathSciNet  MATH  Google Scholar 

  19. Aslanpour MS, Ghobaei-Arani M, Toosi AN (2017) Auto-scaling web applications in clouds: a cost-aware approach. J Netw Comput Appl 95:26–41

    Article  Google Scholar 

  20. Beloglazovy A, Buyya R (2011) 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):1–24

    Google Scholar 

  21. Silva Filho MC, Oliveira RL, Monteiro CC, Inácio PR, Freire MM (2017) CloudSim plus: a cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness. In: 2017 IFIP/IEEE symposium on integrated network and service management pp 400–406

  22. Silva F, Manoel C, Oliveria LR, Monteiro CC, Inacio RMP (2019) CloudSim Plus Documentation. Press Release

  23. Nehra P, Nagaraju A (2019) Sustainable energy consumption modeling for cloud data centers. In: 2019 IEEE 5th international conference for convergence in technology (I2CT), pp 1–4

  24. Uchechukwu A, Li K, Shen Y (2014) Energy consumption in cloud computing data centers. Int J Cloud Comput Serv Sci (IJ-CLOSER) 3(3):31–48

    Google Scholar 

  25. Beloglazov A, Buyya R, Lee YC, Zomaya A (2011) A taxonomy and survey of energy-efficient data centers and cloud computing systems. Adv Comput 82:47–111

    Article  Google Scholar 

  26. Beloglazov A, Buyya R (2012) 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):1366–79

    Article  Google Scholar 

  27. Malhotra L, Agarwal D, Jaiswal A (2014) Virtualization in cloud computing. J Inf Tech Softw Eng 4(2):1–3

    Google Scholar 

  28. 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

  29. Chen F, Schneider JG, Yang Y, Grundy J, He Q (2012) An energy consumption model and analysis tool for cloud computing environments. In: 2012 First international workshop on green and sustainable software (GREENS) pp 45–50

  30. Yamini B, Selvi DV (2010) Cloud virtualization: a potential way to reduce global warming, Recent Adv Space Technol Serv Clim Change, pp 55–57

  31. Masdari M, Nabavi SS, Ahmadi V (2016) An overview of virtual machine placement schemes in cloud computing. J Netw Comput Appl 66:106–27

    Article  Google Scholar 

  32. Panigrahy R, Talwar K, Uyeda L, Wieder U (2011) Heuristics for vector bin packing. research microsoft. com

  33. Ismaeel S, Karim R, Miri A (2018) Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres. J Cloud Comput, pp 1–28

  34. Shirvastava S, Dubey R, Shrivastava M (2017) Best fit based VM allocation for cloud resource allocation. Int J Comput Appl 158(9)

  35. Pandaba P, Behera PK, Ray BNB (2016) Modified round robin algorithm for resource allocation in cloud computing. Proc Comput Sci, pp 878–890

  36. Sumathy S (2017) Dynamic virtual machine allocation policy in cloud computing complying with service level agreement using CloudSim. In: IOP conference series: materials science and engineering, vol 263, Issue 4

  37. Chen J, Du T, Xiao G (2021) A multi-objective optimization for resource allocation of emergent demands in cloud computing. J Cloud Comput 10(1):1–17

    Article  Google Scholar 

  38. Talwani S, Alhazmi K, Singla J, Alyamani HJ, Bashir KA (2011) VAllocation and migration of virtual machines using machine learning. CMC-Comput Mater Continua 70(2):3349–3364

    Article  Google Scholar 

  39. Stergiou C, Psannis KE, Gupta BB, Ishibashi Y, Bashir KA (2018) Security, privacy and efficiency of sustainable cloud computing for big data and IoT. Sustain Comput Inf Syst 19:174–184

    Google Scholar 

  40. Stergiou CL, Psannis KE, Gupta BB (2021) VAllocation and migration of virtual machines using machine learning, InFeMo: flexible big data management through a federated cloud system. ACM Trans Int Technol(TOIT), 22(2): 1–22

  41. Memos VA, Psannis KE, Goudos SK, Kyriazakos S (2021) An enhanced and secure cloud infrastructure for e-health data transmission. Wirel Pers Commun 117(1):109–127

    Article  Google Scholar 

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Acknowledgements

We thank Dr. A. Nagraju, Assistant Professor for their guidance and assistance with methodology of this paper and anonymous reviewers for their valuable comments and feedback to improve the quality of the paper

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  1. Computer Science, Central University of Rajasthan, NH-8, Bandarsindri, Kishangarh, Ajmer, 305817, Rajasthan, India

    P. Nehra & Nishtha Kesswani

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  1. P. Nehra
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  2. Nishtha Kesswani
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Correspondence to Nishtha Kesswani.

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Nehra, P., Kesswani, N. Efficient resource allocation and management by using load balanced multi-dimensional bin packing heuristic in cloud data centers. J Supercomput 79, 1398–1425 (2023). https://doi.org/10.1007/s11227-022-04707-w

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