International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

International Journal of Computer Networks and Applications (IJCNA)

International Journal of Computer Networks and Applications (IJCNA)

Published By EverScience Publications

ISSN : 2395-0455

An Efficient Load Balancing HBLBACO Approach Using Hybrid BAT and LBACO Algorithm in Cloud Environment

Author NameAuthor Details

Shalu Rani, Dharminder Kumar, Sakshi Dhingra

Shalu Rani[1]

Dharminder Kumar[2]

Sakshi Dhingra[3]

[1]Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India.

[2]Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India.

[3]Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India.

Abstract

Cloud computing has emerged as a crucial paradigm for delivering scalable and efficient services to many users. Load balancing in cloud environments presents several challenges, such as optimizing makespan, degree of imbalance, standard deviation, enhancing system performance and processing speed, and ensuring a reliable cloud infrastructure. These challenges are exacerbated by dynamic and unpredictable workloads, which can lead to uneven distribution of tasks and underutilization or overloading of resources. To address the challenges proposed by dynamic and unpredictable workloads, various load-balancing algorithms have been proposed. This work presents a novel approach called the HBLBACO (Hybrid BAT and LBACO) algorithm to balance the load on cloud, which combines the strengths of the Bat algorithm (BA) and the Load Balancing Ant Colony Optimization (LBACO) algorithm that is local optima and global optima respectively to achieve improved load distribution in cloud environments. To analyse the proposed algorithm, extensive experiments were conducted using CloudSim simulation environments. The experimental results demonstrate that the HBLBACO algorithm reduces makespan, degree of imbalance, standard deviation and maximized processing speed. It effectively adapts to dynamic workload changes and achieves a more balanced distribution of tasks across VMs, leading to improved system performance. The results confirm that the proposed approach outperforms 8%, 68%, 71%, 81% then LBACO, 2%, 21%, 43%, 96% then ACO and 53% ,96%, 98% then PSO algorithm in terms of makespan, degree of imbalance, standard deviation and processing speed respectively.

Index Terms

Cloud Computing

Cloud Load Balancing

HBLBACO Algorithm

BAT Algorithm

LBACO Algorithm

ACO Algorithm

Reference

  1. 1.
    Panwar, A. Singh, A. Dixit, and G. Parashar, “Cloud Computing and Load Balancing: A Review,” 2022 Int. Conf. Comput. Intell. Sustain. Eng. Solut. (CISES), 2022, pp. 334–343, 2022, doi: 10.1109/cises54857.2022.9844367.
  2. 2.
    S. Rani, D. Kumar, and S. Dhingra, “A review on dynamic load balancing algorithms,” 3rd IEEE 2022 Int. Conf. Comput. Commun. Intell. Syst. ICCCIS 2022, pp. 515–520, 2022, doi: 10.1109/ICCCIS56430.2022.10037671.
  3. 3.
    A. Hamidi, M. K. Goal, and R. Astya, “Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm: A Review,” Proc. 2022 9th Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2022, pp. 639–643, 2022, doi: 10.23919/INDIACom54597.2022.9763131.
  4. 4.
    S. Khare, U. Chourasia, and A. J. Deen, “Load Balancing in Cloud Computing,” in Proceedings of the International Conference on Cognitive and Intelligent Computing, 2022, pp. 601–608.
  5. 5.
    S. T. Waghmode and B. M. Patil, “Adaptive Load Balancing in Cloud Computing Environment,” Int. J. Intell. Syst. Appl. Eng., vol. 11, pp. 209–217, 2023.
  6. 6.
    M. A. Elmagzoub, D. Syed, A. Shaikh, N. Islam, A. Alghamdi, and S. Rizwan, “A survey of swarm intelligence based load balancing techniques in cloud computing environment,” Electron., vol. 10, no. 21, 2021, doi: 10.3390/electronics10212718.
  7. 7.
    H. Xue, K. T. Kim, and H. Y. Youn, “Dynamic load balancing of software-defined networking based on genetic-ant colony optimization,” Sensors (Switzerland), vol. 19, no. 2, 2019, doi: 10.3390/s19020311.
  8. 8.
    A. Gupta and H. S. Bhadauria, “Honey Bee Based Improvised BAT Algorithm for Cloud Task Scheduling,” Int. J. Comput. Networks Appl., vol. 10, no. 4, pp. 494-510journal, 2023, doi: 10.22247/ijcna/2023/223310.
  9. 9.
    A. M. Manasrah and H. B. Ali, “Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing,” Wirel. Commun. Mob. Comput., vol. 2018, 2018, doi: 10.1155/2018/1934784.
  10. 10.
    P. Jain and S. K. Sharma, “A Load Balancing Aware Task Scheduling using Hybrid Firefly Salp Swarm Algorithm in Cloud Computing,” Int. J. Comput. Networks Appl., vol. 10, no. 6, pp. 914–933, 2023, doi: 10.22247/ijcna/2023/223686.
  11. 11.
    H. Xing, F. Song, L. Yan, and W. Pan, “A modified artificial bee colony algorithm for load balancing in network-coding-based multicast,” Soft Comput., vol. 23, no. 15, pp. 6287–6305, 2019, doi: 10.1007/s00500-018-3284-9.
  12. 12.
    H. Saini, G. Singh, and M. Rohil, “Design of Hybrid Metaheuristic Optimization Algorithm for Trust-Aware Privacy Preservation in Cloud Computing,” Int. J. Comput. Networks Appl., vol. 10, no. 6, pp. 934–946, 2023, doi: 10.22247/ijcna/2023/223690.
  13. 13.
    K. Dasgupta, B. Mandal, P. Dutta, J. K. Mandal, and S. Dam, “A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing,” Procedia Technol., vol. 10, pp. 340–347, 2013, doi: 10.1016/j.protcy.2013.12.369.
  14. 14.
    M. Adhikari, S. Nandy, and T. Amgoth, “Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud,” J. Netw. Comput. Appl., vol. 128, pp. 64–77, 2019, doi: https://doi.org/10.1016/j.jnca.2018.12.010.
  15. 15.
    W. Saber, W. Moussa, A. M. Ghuniem, and R. Rizk, “Hybrid load balance based on genetic algorithm in cloud environment,” Int. J. Electr. Comput. Eng., vol. 11, no. 3, pp. 2477–2489, 2021, doi: 10.11591/ijece.v11i3.pp2477-2489.
  16. 16.
    P. Neelima and A. R. M. Reddy, “An efficient load balancing system using adaptive dragonfly algorithm in cloud computing,” Cluster Comput., vol. 23, no. 4, pp. 2891–2899, 2020, doi: 10.1007/s10586-020-03054-w.
  17. 17.
    Y. Shi and K. Qian, “LBMM: A Load Balancing Based Task Scheduling Algorithm for Cloud,” in Advances in Information and Communication, 2020, pp. 706–712.
  18. 18.
    M. Haghi Kashani and E. Mahdipour, “Load Balancing Algorithms in Fog Computing: A Systematic Review,” IEEE Trans. Serv. Comput., vol. 1374, no. c, pp. 1–18, 2022, doi: 10.1109/TSC.2022.3174475.
  19. 19.
    S. Abdolhosseini and M. T. Kheirabadi, “Scheduling Independent Parallel Jobs in Cloud Computing?: A Survey,” vol. 11, no. 3, pp. 11–21, 2019.
  20. 20.
    Z. Shafahi and A. Yari, “An efficient task scheduling in cloud computing based on ACO algorithm,” 2021 12th Int. Conf. Inf. Knowl. Technol. IKT 2021, pp. 72–77, 2021, doi: 10.1109/IKT54664.2021.9685674.
  21. 21.
    X. S. Yang, “A new metaheuristic Bat-inspired Algorithm,” Stud. Comput. Intell., vol. 284, pp. 65–74, 2010, doi: 10.1007/978-3-642-12538-6_6.
  22. 22.
    B. Xing and W.-J. Gao, Innovative Computational Intelligence?: A Rough Guide to 134 Clever Algorithms. 2014.
  23. 23.
    B. Mallikarjuna, K. H. Reddy, and O. Hemakesavulu, “Economic Load Dispatch Problem with Valve – Point Effect Using a Binary Bat Algorithm,” ACEEE Int. J. Electr. Power Eng., vol. 4, no. 3, pp. 33–38, 2013.
  24. 24.
    A. K. Jayswal, “Efficient task allocation for cloud using bat Algorithm,” PDGC 2020 - 2020 6th Int. Conf. Parallel, Distrib. Grid Comput., pp. 186–190, 2020, doi: 10.1109/PDGC50313.2020.9315845.
  25. 25.
    A. K. Jayswal and D. K. Lobiyal, “Fault Aware BAT Algorithm for Task Scheduling in Cloud,” Proc. 2021 10th Int. Conf. Syst. Model. Adv. Res. Trends, SMART 2021, pp. 104–108, 2021, doi: 10.1109/SMART52563.2021.9676253.
  26. 26.
    Z. Zhang and X. Zhang, “A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation,” ICIMA 2010 - 2010 2nd Int. Conf. Ind. Mechatronics Autom., vol. 2, pp. 240–243, 2010, doi: 10.1109/ICINDMA.2010.5538385.
  27. 27.
    S. Fidanova and M. Durchova, “Ant algorithm for grid scheduling problem,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3743 LNCS, pp. 405–412, 2006, doi: 10.1007/11666806_46.
  28. 28.
    K. Li, G. Xu, G. Zhao, Y. Dong, and D. Wang, “Cloud task scheduling based on load balancing ant colony optimization,” Proc. - 2011 6th Annu. ChinaGrid Conf. ChinaGrid 2011, pp. 3–9, 2011, doi: 10.1109/ChinaGrid.2011.17.
  29. 29.
    A. Hota, S. Mohapatra, and S. Mohanty, Survey of Different Load Balancing Approach-Based Algorithms in Cloud Computing: A Comprehensive Review, vol. 711. Springer Singapore, 2019. doi: 10.1007/978-981-10-8055-5_10.
  30. 30.
    B. Wang and J. Li, “Load balancing task scheduling based on Multi-Population Genetic Algorithm in cloud computing,” Chinese Control Conf. CCC, vol. 2016-Augus, pp. 5261–5266, 2016, doi: 10.1109/ChiCC.2016.7554174.
  31. 31.
    K. Pradeep and D. Pravakar, “Exploration on Task Scheduling using Optimization Algorithm in Cloud computing,” 2022 6th Int. Conf. Trends Electron. Informatics, ICOEI 2022 - Proc., no. Icoei, pp. 874–877, 2022, doi: 10.1109/ICOEI53556.2022.9777120.
  32. 32.
    M. Gamal, R. Rizk, H. Mahdi, and B. E. Elnaghi, “Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing,” IEEE Access, vol. 7, pp. 42735–42744, 2019, doi: 10.1109/ACCESS.2019.2907615.
  33. 33.
    A. Singh, A. Meyyazhagan, and S. Verma, “Nature-Inspired Computing: Bat Echolocation to BAT Algorithm,” in Nature-Inspired Intelligent Computing Techniques in Bioinformatics, K. Raza, Ed. Singapore: Springer Nature Singapore, 2023, pp. 163–174. doi: 10.1007/978-981-19-6379-7_9.
  34. 34.
    D. Karaboga and C. Ozturk, “A novel clustering approach: Artificial Bee Colony (ABC) algorithm,” Appl. Soft Comput. J., vol. 11, no. 1, pp. 652–657, 2011, doi: 10.1016/j.asoc.2009.12.025.
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