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An efficient firefly and honeybee based load balancing mechanism in cloud infrastructure

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Abstract

Dynamic workloads with varying parameters are delivered to data centers to be scheduled on virtual machines (VMs). These unreliable, inconsistent, shockingly rising workloads with changing resource requisites may cause data center servers to become imbalanced. Consequently, the servers' resource utilization and QoS get degraded which further subsides the energy usage. To meet the requirement for variable on-demand resource provisioning, the current infrastructure must be virtualized to escalate its capability and capacity. Furthermore, because of the existence of competing balancing constraints, load balancing in cloud systems is NP-hard. An efficient load-balancing strategy in coalition with a scheduling technique based on the fusion of the Firefly (FF) and Honeybee algorithm (HA) is implemented to resolve the shortcomings of load balancing. This article proposes an efficient Firefly and Honeybee-based Load Balancing algorithm (FHLBA), to optimize average load, response time, and turnaround time. This methodology not only improvises resource utilization and throughput but also depreciates the makespan, degree of imbalance. It initially executes the FF algorithm to explore potential search space and identify the best feasible mapping of tasks to VMs. Further, the HA technique is applied to eliminate over-usage and under-usage of resources and optimize resource usage as per the workload. The efficacy of the proposed technique is appraised through simulation of the independent and non-preemptive tasks on Cloudsim. A comparison with different mechanisms such as RR, FCFS, SJF, GA, IPSO, IPSO-Firefly, Firefly, OLB, and JAYA is given. The simulation results show the least average load of 0.238 ms, minimum average response time of 13.56 ms, memory usage up to 93%, effective CPU utilization of 98%, a throughput of 73%, and a makespan time of 148 ms, degree of imbalance of 172.01 is achieved with a variable volume of tasks and VMs.

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References

  1. Srivastava, A., Kumar, N.: Resource management techniques in cloud computing: a state of art. ICIC Express Lett. 14(9), 909–916 (2020)

    Google Scholar 

  2. Brown, E., Swenson, G.: Final Version of NIST Cloud Computing Definition Published. NIST, Gaithersburg (2017)

    Google Scholar 

  3. Pradhan, P., Behera, P.K., Ray, B.N.B.: Modified round robin algorithm for resource allocation in cloud computing. Procedia Comput Sci 85, 878–890 (2016). https://doi.org/10.1016/j.procs.2016.05.278

    Article  Google Scholar 

  4. Srivastava, A., Kumar, N.: An energy efficient robust resource provisioning based on improved PSO-ANN. Int. J. Inf. Technol. 15(1), 107–117 (2023). https://doi.org/10.1007/s41870-022-01148-9

    Article  Google Scholar 

  5. Mishra, S.K., Sahoo, B., Parida, P.P.: Load balancing in cloud computing: a big picture. J. King Saud Univ.-Comput. Inf. Sci. 32(2), 149–158 (2020). https://doi.org/10.1016/j.jksuci.2018.01.003

    Article  Google Scholar 

  6. Magalhães, D., Calheiros, R.N., Buyya, R., Gomes, D.G.: Workload modelling for resource usage analysis and simulation in cloud computing. Comput. Electr. Eng. 47, 69–81 (2015). https://doi.org/10.1016/j.compeleceng.2015.08.016

    Article  Google Scholar 

  7. Deepa, T., Cheelu, D.: A comparative study of static and dynamic load balancing algorithms in cloud computing. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 3375–3378 (2017). https://doi.org/10.1109/ICECDS.2017.8390086

  8. Hamadah, S.: A survey: a comprehensive study of static, dynamic and hybrid load balancing algorithms. In: International Journal of Computer Science and Information Technology & Security (IJCSITS), pp. 2249–9555 (2017).

  9. Srivastava, A., Kumar, N.: Multi-objective binary whale optimization-based virtual machine allocation in cloud environments. Int. J. Swarm Intell. Res. 14(1), 1–23 (2023). https://doi.org/10.4018/IJSIR.317111

    Article  Google Scholar 

  10. Marichelvam, M.K., Prabaharan, T., Yang, X.S.: A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems. IEEE Trans. Evol. Comput. 18(2), 301–305 (2013). https://doi.org/10.1109/TEVC.2013.2240304

    Article  Google Scholar 

  11. de Vries, H., Biesmeijer, J.C.: Modelling collective foraging by means of individual behaviour rules in honey-bees. Behav. Ecol. Sociobiol. 44(2), 109–124 (1998). https://doi.org/10.1007/s002650050522

    Article  Google Scholar 

  12. Afzal, S., Kavitha, G.: Load balancing in cloud computing—a hierarchical taxonomical classification. J. Cloud Comput. 8(1), 1–24 (2019). https://doi.org/10.1186/s13677-019-0146-7

    Article  Google Scholar 

  13. Singh, A.N., Prakash, S.: WAMLB: weighted active monitoring load balancing in cloud computing. In: Big Data Analytics, pp. 677–685. Springer, Singapore (2018)

  14. Kumar, M., Dubey, K., Sharma, S.C.: Elastic and flexible deadline constraint load balancing algorithm for cloud computing. Procedia Comput. Sci. 125, 717–724 (2018). https://doi.org/10.1016/j.procs.2017.12.092

    Article  Google Scholar 

  15. Chen, S.L., Chen, Y.Y., Kuo, S.H.: CLB: a novel load balancing architecture and algorithm for cloud services. Comput. Electr. Eng. 58, 154–160 (2017). https://doi.org/10.1016/j.compeleceng.2016.01.029

    Article  Google Scholar 

  16. Muthusamy, G., Chandran, S.R.: Cluster-based task scheduling using K-means clustering for load balancing in cloud datacenters. J. Internet Technol. 22(1), 121–130 (2021)

    Google Scholar 

  17. Adhikari, M., Amgoth, T.: Heuristic-based load-balancing algorithm for IaaS cloud. Futur. Gener. Comput. Syst. 81, 156–165 (2018). https://doi.org/10.1016/j.future.2017.10.035

    Article  Google Scholar 

  18. Kong, L., Mapetu, J.P.B., Chen, Z.: Heuristic load balancing based zero imbalance mechanism in cloud computing. J. Grid Comput. 18(1), 123–148 (2020). https://doi.org/10.1007/s10723-019-09486-y

    Article  Google Scholar 

  19. Haidri, R.A., Katti, C.P., Saxena, P.C.: Capacity based deadline aware dynamic load balancing (CPDALB) model in cloud computing environment. Int. J. Comput. Appl. 43(10), 987–1001 (2021). https://doi.org/10.1080/1206212X.2019.1640932

    Article  Google Scholar 

  20. Waghmode, S.T., Patil, B.M.: Adaptive load balancing in cloud computing environment. Int. J. Intell. Syst. Appl. Eng. 11(1s), 209–217 (2023)

    Google Scholar 

  21. Khair, Y., Benlabbes, H.: Opportunistic load balancing for virtual machines scheduling in a cloud environment. Eng. Proc. 29(1), 1–6 (2023). https://doi.org/10.3390/engproc2023029001

    Article  Google Scholar 

  22. Mohanty, S., Patra, P.K., Ray, M., Mohapatra, S.: An approach for load balancing in cloud computing using JAYA algorithm. Int. J. Inf. Technol. Web Eng. 14(1), 27–41 (2019). https://doi.org/10.4018/IJITWE.2019010102

    Article  Google Scholar 

  23. Bezdan, T., Zivkovic, M., Bacanin, N., Strumberger, I., Tuba, E., Tuba, M.: Multi-objective task scheduling in cloud computing environment by hybridized bat algorithm. J. Intell. Fuzzy Syst. 42(1), 411–423 (2022). https://doi.org/10.3233/JIFS-219200

    Article  Google Scholar 

  24. Shafiq, D.A., Jhanjhi, N.Z., Abdullah, A., Alzain, M.A.: A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access 9, 41731–41744 (2021). https://doi.org/10.1109/ACCESS.2021.3065308

    Article  Google Scholar 

  25. Belgacem, A., Beghdad-Bey, K.: Multi-objective workflow scheduling in cloud computing: trade-off between makespan and cost. Clust. Comput. 25(1), 579–595 (2022). https://doi.org/10.1007/s10586-021-03432-y

    Article  Google Scholar 

  26. Huang, X., Lin, Y., Zhang, Z., Guo, X., Su, S.: A gradient-based optimization approach for task scheduling problem in cloud computing. Clust. Comput. 25(5), 3481–3497 (2022). https://doi.org/10.1007/s10586-022-03580-9

    Article  Google Scholar 

  27. Asghari, A., Sohrabi, M.K., Yaghmaee, F.: Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm. J. Supercomput. 77(3), 2800–2828 (2021). https://doi.org/10.1007/s11227-020-03364-1

    Article  Google Scholar 

  28. Princess, G.A.P., Radhamani, A.S.: A hybrid meta-heuristic for optimal load balancing in cloud computing. J. Grid Comput. 19(2), 1–22 (2021). https://doi.org/10.1007/s10723-021-09560-4

    Article  Google Scholar 

  29. Jena, U.K., Das, P.K., Kabat, M.R.: Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. J. King Saud Univ.-Comput. Inf. Sci. 34(6), 2332–2342 (2022). https://doi.org/10.1016/j.jksuci.2020.01.012

    Article  Google Scholar 

  30. Saravanan, G., Neelakandan, S., Ezhumalai, P., Maurya, S.: Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing. J. Cloud Comput. 12(1), 24 (2023). https://doi.org/10.1186/s13677-023-00401-1

    Article  Google Scholar 

  31. Durga Devi, T.J.B., Subramani, A., Anitha, P.: Modified adaptive neuro fuzzy inference system based load balancing for virtual machine with security in cloud computing environment. J. Ambient. Intell. Humaniz. Comput. 12(3), 3869–3876 (2021). https://doi.org/10.1007/s12652-020-01728-2

    Article  Google Scholar 

  32. Abualigah, L., Alkhrabsheh, M.: Amended hybrid multi-verse optimizer with genetic algorithm for solving task scheduling problem in cloud computing. J. Supercomput. 78(1), 740–765 (2022). https://doi.org/10.1007/s11227-021-03915-0

    Article  Google Scholar 

  33. Golchi, M.M., Saraeian, S., Heydari, M.: A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: performance evaluation. Comput. Netw. 162, 106860 (2019). https://doi.org/10.1016/j.comnet.2019.106860

    Article  Google Scholar 

  34. Malik, M., Suman.: Lateral wolf based particle swarm optimization (LW-PSO) for load balancing on cloud computing. Wirel. Personal Commun. 125(2), 1125–1144 (2022). https://doi.org/10.1007/s11277-022-09592-3

  35. Neelakantan, P., Yadav, N.S.: An optimized load balancing strategy for an enhancement of cloud computing environment. Wirel. Personal Commun. (2023). https://doi.org/10.1007/s11277-023-10520-2

    Article  Google Scholar 

  36. Balaji, K., Kiran, P.S., Kumar, M.S.: An energy efficient load balancing on cloud computing using adaptive cat swarm optimization. Mater. Today (2021). https://doi.org/10.1016/j.matpr.2020.11.106

    Article  Google Scholar 

  37. Thakur, A., Goraya, M.S.: RAFL: a hybrid metaheuristic based resource allocation framework for load balancing in cloud computing environment. Simul. Model. Pract. Theory 116, 102485 (2022). https://doi.org/10.1016/j.simpat.2021.102485

    Article  Google Scholar 

  38. Kumar, M., Sharma, S.C.: Deadline constrained based dynamic load balancing algorithm with elasticity in cloud environment. Comput. Electr. Eng. 69, 395–411 (2018). https://doi.org/10.1016/j.compeleceng.2017.11.018

    Article  Google Scholar 

  39. Pezzella, F., Morganti, G., Ciaschetti, G.: A genetic algorithm for the flexible job-shop scheduling problem. Comput. Oper. Res. 35(10), 3202–3212 (2008). https://doi.org/10.1016/j.cor.2007.02.014

    Article  Google Scholar 

  40. Li, J., Pan, Q., Xie, S.: An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Appl. Math. Comput. 218(18), 9353–9371 (2012). https://doi.org/10.1016/j.amc.2012.03.018

    Article  MathSciNet  Google Scholar 

  41. Mishra, K., Pati, J., Majhi, S.K.: A dynamic load scheduling in IaaS cloud using binary JAYA algorithm. J. King Saud Univ. Comput. Inf. Sci. 34(8), 4914–4930 (2022). https://doi.org/10.1016/j.jksuci.2020.12.001

    Article  Google Scholar 

  42. Abualigah, L., Diabat, A.: A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Comput. 24, 205–223 (2021). https://doi.org/10.1007/s10586-020-03075-5

    Article  Google Scholar 

  43. Khallouli, W., Huang, J.: Cluster resource scheduling in cloud computing: literature review and research challenges. J. Supercomput. 78, 6898–6943 (2022). https://doi.org/10.1007/s11227-021-04138-z

    Article  Google Scholar 

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  1. Department of Computer Science, Babasaheb Bhimrao Ambedkar University (A Central University), Lucknow, India

    Ankita Srivastava & Narander Kumar

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  1. Ankita Srivastava
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Contributions

The development of theory, the initial computation and the simulation was performed by AS. The data analysis was done by NK. NK encouraged to investigate and supervised the findings of the work. The first draft was prepared by AS which is further revised critically by NK to prepare the final manuscript. Both the authors read and approved the final manuscript.

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Correspondence to Ankita Srivastava.

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Srivastava, A., Kumar, N. An efficient firefly and honeybee based load balancing mechanism in cloud infrastructure. Cluster Comput 27, 2805–2827 (2024). https://doi.org/10.1007/s10586-023-04118-3

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