International Journal of Computer Networks and Applications (IJCNA)

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International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

SOAVMP: Multi-Objective Virtual Machine Placement in Cloud Computing Based on the Seagull Optimization Algorithm

Author NameAuthor Details

Aristide Ndayikengurukiye, Rim Doukha, Eric Niyukuri, Eric Muheto,Abderrahmane Ez-zahout, Fouzia Omary

Aristide Ndayikengurukiye[1]

Rim Doukha[2]

Eric Niyukuri[3]

Eric Muheto[4]

Abderrahmane Ez-zahout[5]

Fouzia Omary[6]

[1]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

[2]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

[3]Faculty of Engineering Sciences, University of Burundi, Bujumbura, Burundi.

[4]Morgan Stanley, Montréal, Canada.

[5]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

[6]Intelligent Processing and Security of Systems Team, Faculty of Sciences, Mohammed V University, Rabat, Morocco.

Cite this Article

DOI: 10.22247/ijcna/2024/24

Pages : 375-389

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Abstract

Virtual machine placement (VMP) involves selecting the most appropriate physical machine (PM) to run a virtual machine (VM) in cloud data centers (CDCs). Unfortunately, current VMP methods only consider limited resources, resulting in load imbalance and unnecessary activation of certain PMs in the data center (DC). This paper proposes a new approach called Multi-Objective Seagull Optimization Algorithm Virtual Machine (MOSOAVMP) to address these issue s and enhance resource management in CDCs. The aim is to optimize resource utilization, minimize energy consumption, reduce SLA violations, and improve overall DC efficiency. The aim is to achieve an optimal deployment that will meet these different objectives while minimizing the costs associated with operating the CDCs. The results show the proposed MOSOAVMP's efficiency compared with existing algorithms for the different measurements considered. These experimental results show that MOSOAVMP reduces resource wastages, and energy consumption by 5.44%, improves average CPU usage by 14.84%, memory usage by 11.54%, average storage usage by 5.37%, and average bandwidth usage by 6.88%.

Index Terms

Cloud Computing

Seagull Optimization Algorithm

Metaheuristics Algorithm

SLA

Virtual Machine Placement

Data Center

Power Consumption

Reference

  1. 1.
    M. Masdari and M. Zangakani, “Green Cloud Computing Using Proactive Virtual Machine Placement: Challenges and Issues,” J Grid Comput, 2019, doi: 10.1007/s10723-019-09489-9.
  2. 2.
    J. Singh and N. K. Walia, “A Comprehensive Review of Cloud Computing Virtual Machine Consolidation,” IEEE Access, vol. 11. Institute of Electrical and Electronics Engineers Inc., pp. 106190–106209, 2023. doi: 10.1109/ACCESS.2023.3314613.
  3. 3.
    A. Ndayikengurukiye, A. Ez-Zahout, A. Aboubakr, Y. Charkaoui, and O. Fouzia, “Resource Optimisation in Cloud Computing: Comparative Study of Algorithms Applied to Recommendations in a Big Data Analysis Architecture,” Journal of Automation, Mobile Robotics and Intelligent Systems, vol. 2021, no. 4, pp. 65–75, 2021, doi: 10.14313/JAMRIS/4-2021/28.
  4. 4.
    S. Gharehpasha, M. Masdari, and A. Jafarian, “Power efficient virtual machine placement in cloud data centers with a discrete and chaotic hybrid optimization algorithm,” Cluster Comput, vol. 24, no. 2, pp. 1293–1315, Jun. 2021, doi: 10.1007/s10586-020-03187-y.
  5. 5.
    R. Regaieg, M. Koubàa, Z. Ales, and T. Aguili, “Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers,” Computing, vol. 103, no. 6, pp. 1255–1279, Jun. 2021, doi: 10.1007/s00607-021-00915-z.
  6. 6.
    K. RahimiZadeh and A. Dehghani, “Design and evaluation of a joint profit and interference-aware VMs consolidation in IaaS cloud datacenter,” Cluster Comput, vol. 24, no. 4, pp. 3249–3275, Dec. 2021, doi: 10.1007/s10586-021-03310-7.
  7. 7.
    A. Beloglazov, J. Abawajy, and R. Buyya, “Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing,” Future Generation Computer Systems, vol. 28, no. 5, pp. 755–768, May 2012, doi: 10.1016/j.future.2011.04.017.
  8. 8.
    Z. Xiao, J. Jiang, Y. Zhu, Z. Ming, S. Zhong, and S. Cai, “A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory,” Journal of Systems and Software, vol. 101, pp. 260–272, Mar. 2015, doi: 10.1016/j.jss.2014.12.030.
  9. 9.
    J. Lu, W. Zhao, H. Zhu, J. Li, Z. Cheng, and G. Xiao, “Optimal machine placement based on improved genetic algorithm in cloud computing,” Journal of Supercomputing, vol. 78, no. 3, pp. 3448–3476, Feb. 2022, doi: 10.1007/s11227-021-03953-8.
  10. 10.
    L. Caviglione, M. Gaggero, M. Paolucci, and R. Ronco, “Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters,” Soft comput, vol. 25, no. 19, pp. 12569–12588, Oct. 2021, doi: 10.1007/s00500-020-05462-x.
  11. 11.
    F. Alharbi, Y. C. Tian, M. Tang, M. H. Ferdaus, W. Z. Zhang, and Z. G. Yu, “Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers,” Cluster Comput, vol. 24, no. 2, pp. 1255–1275, Jun. 2021, doi: 10.1007/s10586-020-03186-z.
  12. 12.
    M. Ghetas, “A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing,” Neural Comput Appl, vol. 33, no. 17, pp. 11011–11025, Sep. 2021, doi: 10.1007/s00521-020-05559-2.
  13. 13.
    S. Y. Rashida, M. Sabaei, M. M. Ebadzadeh, and A. M. Rahmani, “A memetic grouping genetic algorithm for cost efficient VM placement in multi-cloud environment,” Cluster Comput, vol. 23, no. 2, pp. 797–836, Jun. 2020, doi: 10.1007/s10586-019-02956-8.
  14. 14.
    H. M. R. Al-Khafaji, “Improving Quality Indicators of the Cloud-Based IoT Networks Using an Improved Form of Seagull Optimization Algorithm,” Future Internet, vol. 14, no. 10, Oct. 2022, doi: 10.3390/fi14100281.
  15. 15.
    S. Nabavi, L. Wen, S. S. Gill, and M. Xu, “Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers,” Internet of Things and Cyber-Physical Systems, vol. 3, pp. 28–36, 2023, doi: 10.1016/j.iotcps.2023.01.002.
  16. 16.
    G. Dhiman, M. Garg, A. Nagar, V. Kumar, and M. Dehghani, “A novel algorithm for global optimization: Rat Swarm Optimizer,” J Ambient Intell Humaniz Comput, vol. 12, no. 8, pp. 8457–8482, Aug. 2021, doi: 10.1007/s12652-020-02580-0.
  17. 17.
    S. Mohapatra and P. Mohapatra, “American zebra optimization algorithm for global optimization problems,” Sci Rep, vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-31876-2.
  18. 18.
    Z. Ma, G. Wu, P. N. Suganthan, A. Song, and Q. Luo, “Performance assessment and exhaustive listing of 500+ nature-inspired metaheuristic algorithms,” Swarm Evol Comput, vol. 77, Mar. 2023, doi: 10.1016/j.swevo.2023.101248.
  19. 19.
    R. Boopathi and E. S. Samundeeswari, “Amended Hybrid Scheduling for Cloud Computing with Real-Time Reliability Forecasting,” International Journal of Computer Networks and Applications, vol. 10, no. 3, pp. 310–324, May 2023, doi: 10.22247/ijcna/2023/221887.
  20. 20.
    P. Jain and S. K. Sharma, “A Load Balancing Aware Task Scheduling using Hybrid Firefly Salp Swarm Algorithm in Cloud Computing,” International Journal of Computer Networks and Applications, vol. 10, no. 6, pp. 914–933, Nov. 2023, doi: 10.22247/ijcna/2023/223686.
  21. 21.
    G. Dhiman and V. Kumar, “Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems,” Knowl Based Syst, vol. 165, pp. 169–196, Feb. 2019, doi: 10.1016/j.knosys.2018.11.024.
  22. 22.
    V. Kumar, Di. Kumar, M. Kaur, Di. Singh, S. A. Idris, and H. Alshazly, “A Novel Binary Seagull Optimizer and its Application to Feature Selection Problem,” IEEE Access, vol. 9, pp. 103481–103496, 2021, doi: 10.1109/ACCESS.2021.3098642.
  23. 23.
    G. Dhiman et al., “EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization,” International Journal of Machine Learning and Cybernetics, vol. 12, no. 2, pp. 571–596, Feb. 2021, doi: 10.1007/s13042-020-01189-1.
  24. 24.
    F. M. Alamgir and M. S. Alam, “An artificial intelligence driven facial emotion recognition system using hybrid deep belief rain optimization,” Multimed Tools Appl, vol. 82, no. 2, pp. 2437–2464, Jan. 2023, doi: 10.1007/s11042-022-13378-x.
  25. 25.
    Q. Wang, M. Zhang, and S. Abdolhosseinzadeh, “Application of modified seagull optimization algorithm with archives in urban water distribution networks: Dealing with the consequences of sudden pollution load,” Heliyon, vol. 10, no. 3, p. e24920, Feb. 2024, doi: 10.1016/j.heliyon.2024.e24920.
  26. 26.
    P. Krishnadoss, V. K. Poornachary, P. Krishnamoorthy, and L. Shanmugam, “Improvised Seagull Optimization Algorithm for Scheduling Tasks in Heterogeneous Cloud Environment,” Computers, Materials and Continua, vol. 74, no. 2, pp. 2461–2478, 2023, doi: 10.32604/cmc.2023.031614.
  27. 27.
    X. Liu, G. Li, and P. Shao, “A Multi-Mechanism Seagull Optimization Algorithm Incorporating Generalized Opposition-Based Nonlinear Boundary Processing,” Mathematics, vol. 10, no. 18, Sep. 2022, doi: 10.3390/math10183295.
  28. 28.
    B. B. Al-Onazi, H. Alshamrani, F. O. Aldaajeh, A. S. A. Aziz, and M. Rizwanullah, “Modified Seagull Optimization with Deep Learning for Affect Classification in Arabic Tweets,” IEEE Access, vol. 11, pp. 98958–98968, 2023, doi: 10.1109/ACCESS.2023.3310873.
  29. 29.
    I. Abdullaev, N. Prodanova, K. A. Bhaskar, E. L. Lydia, S. Kadry, and J. Kim, “Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning,” Computers, Materials and Continua, vol. 76, no. 2, pp. 1463–1477, Aug. 2023, doi: 10.32604/cmc.2023.038417.
  30. 30.
    J. Xue, X. Liu, H. Xu, and D. Zhang, “Research on the seagull optimization algorithm-based convolutional neural network rolling bearing fault diagnosis method,” Engineering Research Express, vol. 5, no. 3, Sep. 2023, doi: 10.1088/2631-8695/acf09a.
  31. 31.
    Q. Xia, Y. Ding, R. Zhang, H. Zhang, S. Li, and X. Li, “Optimal Performance and Application for Seagull Optimization Algorithm Using a Hybrid Strategy,” Entropy, vol. 24, no. 7, Jul. 2022, doi: 10.3390/e24070973.
  32. 32.
    E. S. Ghith and F. A. A. Tolba, “Tuning PID Controllers Based on Hybrid Arithmetic Optimization Algorithm and Artificial Gorilla Troop Optimization for Micro-Robotics Systems,” IEEE Access, vol. 11, pp. 27138–27154, 2023, doi: 10.1109/ACCESS.2023.3258187.
  33. 33.
    M. Hanif, N. Mohammad, K. Biswas, and B. Harun, “Seagull Optimization Algorithm for Solving Economic Load Dispatch Problem,” in 3rd International Conference on Electrical, Computer and Communication Engineering, ECCE 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ECCE57851.2023.10101516.
  34. 34.
    Nair, Dhanya G., KP Sanal Kumar, and S. Anu H. Nair. "Automated Social Distance Recognition and Classification using Seagull Optimization Algorithm with Multilayer Perceptron." Dandao Xuebao/Journal of Ballistics 35.3 (2023): 11-24.
  35. 35.
    A. Mohammadzadeh, D. Javaheri, and J. Artin, “Chaotic hybrid multi-objective optimization algorithm for scientific workflow scheduling in multisite clouds,” Journal of the Operational Research Society, 2023, doi: 10.1080/01605682.2023.2195426.
  36. 36.
    S. Kaushaley, B. Shaw, and J. Ranjan Nayak, “Optimized Machine Learning based forecasting model for Solar Power Generation by using Crow Search Algorithm and Seagull Optimization Algorithm,” 2022, doi: 10.21203/rs.3.rs-1987438/v1.
  37. 37.
    A. Ndayikengurukiye, A. Ez-Zahout, and F. Omary, “An overview of the different methods for optimizing the virtual resources placement in the Cloud Computing,” J Phys Conf Ser, vol. 1743, p. 012030, Jan. 2021, doi: 10.1088/1742-6596/1743/1/012030.
  38. 38.
    S. Omer, S. Azizi, M. Shojafar, and R. Tafazolli, “A priority, power and traffic-aware virtual machine placement of IoT applications in cloud data centers,” Journal of Systems Architecture, vol. 115, May 2021, doi: 10.1016/j.sysarc.2021.101996.
  39. 39.
    S. Azizi, M. Shojafar, J. Abawajy, and R. Buyya, “GRVMP: A Greedy Randomized Algorithm for Virtual Machine Placement in Cloud Data Centers,” IEEE Syst J, vol. 15, no. 2, pp. 2571–2582, Jun. 2021, doi: 10.1109/JSYST.2020.3002721.
  40. 40.
    S. Farzai, M. H. Shirvani, and M. Rabbani, “Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters,” Sustainable Computing: Informatics and Systems, vol. 28, Dec. 2020, doi: 10.1016/j.suscom.2020.100374.
  41. 41.
    T. B. Hewage, S. Ilager, M. A. Rodriguez, and R. Buyya, “CloudSim express: A novel framework for rapid low code simulation of cloud computing environments,” Softw Pract Exp, 2023, doi: 10.1002/spe.3290.
  42. 42.
    I. Behera and S. Sobhanayak, “Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach,” J Parallel Distrib Comput, vol. 183, Jan. 2024, doi: 10.1016/j.jpdc.2023.104766.
  43. 43.
    Y. Yuan et al., “Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications,” J Bionic Eng, vol. 20, no. 4, pp. 1747–1765, Jul. 2023, doi: 10.1007/s42235-023-00359-5.
  44. 44.
    F. A. Zeidabadi, M. Dehghani, P. Trojovský, Š. Hubálovský, V. Leiva, and G. Dhiman, “Archery Algorithm: A Novel Stochastic Optimization Algorithm for Solving Optimization Problems,” Computers, Materials and Continua, vol. 72, no. 1, pp. 399–416, 2022, doi: 10.32604/cmc.2022.024736.
  45. 45.
    W. Zilong and S. Peng, “A Multi-Strategy Dung Beetle Optimization Algorithm for Optimizing Constrained Engineering Problems,” IEEE Access, vol. 11, pp. 98805–98817, 2023, doi: 10.1109/ACCESS.2023.3313930.
  46. 46.
    Y. Du, H. Yuan, K. Jia, and F. Li, “Research on Threshold Segmentation Method of Two-Dimensional Otsu Image Based on Improved Sparrow Search Algorithm,” IEEE Access, vol. 11, pp. 70459–70469, 2023, doi: 10.1109/ACCESS.2023.3293191.
  47. 47.
    Aristide Ndayikengurukiye, Abderrahmane Ez-Zahout, Fouzia Omary, "Optimizing Virtual Machines Placement in a Heterogeneous Cloud Data Center System", International Journal of Computer Networks and Applications (IJCNA), 11(1), PP: 1-12, 2024, DOI: 10.22247/ijcna/2024/224431.
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