Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA)

Abstract

A cloud data center consumes more energy for computation and switching servers between modes. Virtual Machine (VM) migration enhances the execution of cloud server farm in terms of energy proficiency, adaptation to internal failure, and accessibility. Cloud suppliers, be that as it may, ought to likewise enhance for amounts like energy consumption and administrations costs and in this manner, trying to have all the Virtual Machines with the least measures of physical equipment machines conceivable. The part of virtualization is critical and its execution is subjected to VM migration and machine allotment. A greater amount of the energy is caught up in the cloud; consequently, the use of various calculations is required for sparing energy and productivity upgradation in the proposed work. In the proposed work, the Naive Bayes classifier with hybrid optimization using Artificial Bee Colony–Bat Algorithm (ABC–BA) was implemented to reduce the energy consumption in VM migration. The proposed method was evaluated in CloudSim and the performances were compared using performance index such as success &failure rate, and energy consumption. It is observed from the implementation results that the proposed method reduces energy consumption compared to other existing methods. From the implementation outcomes of the proposed work, it was understood that the model was able to achieve the minimum energy consumption and failure rate i.e., 1000–1200 kWh, 0.2 with maximum success rate and accuracy of 1 and 97.77%.

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References

  1. 1.

    Refaat TK, Kantarci B, Mouftah HT (2016) Virtual machine migration and management for vehicular clouds. Veh Commun 4:47–56

    Google Scholar 

  2. 2.

    Liu Y, Gong B, Xing C, Jian Y (2014) A virtual machine migration strategy based on time series workload prediction using cloud model. Math Probl Eng 2014. https://doi.org/10.1155/2014/973069

  3. 3.

    Papadopoulos AV, Maggio M (2015) Virtual machine migration in cloud infrastructures: problem formalization and policies proposal. In: 2015 IEEE 54th Annual Conference on Decision and Control (CDC), IEEE, pp 6698–6705

  4. 4.

    Kaur Ramandeep (2017) A hybrid approach for virtual machine migration in cloud computing environment. J Adv Res Comput Sci Softw Eng 7(4):30–35

    Google Scholar 

  5. 5.

    Alonso-Monsalve S, García-Carballeira F, Calderón A (2018) A heterogeneous mobile cloud computing model for hybrid clouds. Future Generat Comput Syst. https://doi.org/10.1016/j.future.2018.04.005

    Article  Google Scholar 

  6. 6.

    Andonovski G, Mušič G, Škrjanc I (2018) Fault detection through evolving fuzzy cloud-based model. IFAC-PapersOnLine 51(2):795–800

    Article  Google Scholar 

  7. 7.

    Yadav RK, Kushwaha V (2014) An energy preserving and fault tolerant task scheduler in Cloud computing. In: 2014 International Conference on Advances in Engineering and Technology Research (ICAETR), IEEE, pp 1–5

  8. 8.

    Cerroni W, Esposito F (2016) Optimizing live migration of multiple virtual machines. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2567381

    Article  Google Scholar 

  9. 9.

    Akram SA, Ghaleb S, Hamaid SB, Vasanthi V (2017) Survey study of virtual machine migration techniques in cloud computing. Migration 177(2):19–22

    Google Scholar 

  10. 10.

    Smara M, Aliouat M, Pathan ASK, Aliouat Z (2017) Acceptance test for fault detection in component-based cloud computing and systems. Future Gener Comput Syst 70:74–93

    Article  Google Scholar 

  11. 11.

    Han L, Weili C (2015) Research on fault diagnosis of rolling bearing based on wavelet packet energy feature and planar cloud model. In: 2015 12th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), vol 1. IEEE, pp 36–40

  12. 12.

    Satpathy A, Addya SK, Turuk AK, Majhi B, Sahoo G (2017) Crow search based virtual machine placement strategy in cloud data centers with live migration. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2017.12.032

    Article  Google Scholar 

  13. 13.

    Qiu X, Dai Y, Xiang Y, Xing L (2017) Correlation modeling and resource optimization for cloud service with fault recovery. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2017.2691323

    Article  Google Scholar 

  14. 14.

    Wahid F, Kim DH (2016) An efficient approach for energy consumption optimization and management in the residential building using artificial Bee colony and fuzzy logic. Math Probl Eng 2016:1–14

    Article  Google Scholar 

  15. 15.

    Qasem GM, Madhu BK (2017) Proactive fault tolerance in cloud data centers for performance efficiency. Int J Pure Appl Math 117(22):325–329

    Google Scholar 

  16. 16.

    Liu J, Wang S, Zhou A, Kumar S, Yang F, Buyya R (2016) Using proactive fault-tolerance approach to enhance cloud service reliability. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2567392

    Article  Google Scholar 

  17. 17.

    Yang X, Guo S, Yang H (2008) The establishment of energy consumption optimization model based on genetic algorithm. In: IEEE International Conference on Automation and Logistics, 2008. ICAL 2008, IEEE, pp 1426–1431

  18. 18.

    Egwutuoha IP, Chen S, Levy D, Selic B, Calvo R (2013) Energy efficient fault tolerance for high-performance computing (HPC) in the cloud. In: 2013 IEEE Sixth International Conference on Cloud Computing (CLOUD), IEEE pp 762–769

  19. 19.

    Paulraj GJL, Francis SAJ, Peter JD, Jebadurai IJ (2018) A combined forecast-based virtual machine migration in cloud data centers. Comput Electr Eng 69:287–300. https://doi.org/10.1016/j.compeleceng.2018.01.012

    Article  Google Scholar 

  20. 20.

    Noshy M, Ibrahim A, Ali HA (2018) Optimization of live virtual machine migration in cloud computing: a survey and future directions. J Netw Comput Appl 110:1–10. https://doi.org/10.1016/j.jnca.2018.03.002

    Article  Google Scholar 

  21. 21.

    Choudhary A, Govil MC, Singh G, Awasthi LK, Pilli ES, Kapil D (2017) A critical survey of live virtual machine migration techniques. J Cloud Comput 6(1):23

    Article  Google Scholar 

  22. 22.

    Dhanoa IS, Khurmi SS (2015) Analyzing energy consumption during VM live migration. In: 2015 International Conference on Computing, Communication & Automation (ICCCA), IEEE, pp 584–588

  23. 23.

    Ansari S, Hans K, Khatri SK (2017) A Naive Bayes classifier approach for detecting hypervisor attacks in virtual machines. In: 2017 2nd International Conference on Telecommunication and Networks (TEL-NET), IEEE, pp 1–6

  24. 24.

    Chengli FAN, Qiang FU, Guangzheng LONG, Qinghua XING (2018) Hybrid artificial bee colony algorithm with variable neighborhood search and memory mechanism. J Syst Eng Electron 29(2):405–414

    Article  Google Scholar 

  25. 25.

    Abdelaziz A, Elhoseny M, Salama AS, Riad AM, Hassanien AE (2017) Intelligent algorithms for optimal selection of virtual machine in cloud environment, towards enhance healthcare service. In: The 3rd International Conference on Advanced Intelligent Systems and Informatics (AISI2017), Sept. 9–11, 2017, Cairo-Egypt, Springer

  26. 26.

    Kaur P, Rani A (2015) Virtual machine migration in cloud computing. Int J Grid Distrib Comput 8(5):337–342

    Article  Google Scholar 

  27. 27.

    Ganesh A, Sandhya M, Shankar S (2014) A study on fault tolerance methods in cloud computing. In: 2014 IEEE International Advance Computing Conference (IACC), IEEE, pp 844–849

  28. 28.

    Hassan MK, El Desouky AI, Badawy MM, Sarhan AM, Elhoseny M, Gunasekaran M EoT-driven hybrid ambient assisted living framework with naïve Bayes–firefly algorithm. Neural Comput Appl https://doi.org/10.1007/s00521-018-3533-y (in press)

  29. 29.

    Shankar K, Eswaran P (2017) RGB based multiple share creation in visual cryptography with aid of elliptic curve cryptography. China Commun 14(2):118–130

    Article  Google Scholar 

  30. 30.

    Shankar K, Lakshmanaprabu SK, Gupta D, Maseleno A, de Albuquerque VHC (2018) Optimal feature-based multi-kernel SVM approach for thyroid disease classification. J Supercomput. https://doi.org/10.1007/s11227-018-2469-4

    Article  Google Scholar 

  31. 31.

    Lakshmanaprabu SK, Shankar K, Khanna A, Gupta D, Rodrigues JJ, Pinheiro PR, De Albuquerque VHC (2018) Effective features to classify big data using social internet of things. IEEE Access 6:24196–24204

    Article  Google Scholar 

  32. 32.

    Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), Springer, Berlin, pp 65–74

  33. 33.

    Zhao J, Hu L, Ding Y, Xu G, Hu M (2014) A heuristic placement selection of live virtual machine migration for energy-saving in cloud computing environment. PLoS ONE 9(9):e108275

    Article  Google Scholar 

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Correspondence to V. Vijayakumar.

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Karthikeyan, K., Sunder, R., Shankar, K. et al. Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA). J Supercomput 76, 3374–3390 (2020). https://doi.org/10.1007/s11227-018-2583-3

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Keywords

  • Cloud computing
  • Virtual Machine
  • Failure detection
  • Prediction
  • Optimization
  • And energy consumption