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MHDORA-LBA: Dynamic and Optimized Resource-Aware Load Balancing Approach for Resource Allocation

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Abstract

This paper discusses the LB approach as an optimization-based method for dynamic resource allocation and its impacts on stability and profit. Artificial Bee Colony (ABC), which is a swarm intelligence, was founded based on the foraging behaviour of honey bees (HB) to optimise problems. A newly developed strategy based on this concept is the Differential Evolution Optimized (DEO) Dynamic Resource-Aware LB Method that draws from the HB. It operates by integrating two schemas, DRALBA and Hybrid Modified Honey Bee, via DE Algorithm known as HyMHBDE. The resource allocation in this paper is done dynamically with the help of DRALBA. It presents a novel HB that employs the DEO strategy, borrowed from scout bees, to enhance the performance of the standard ABC in exploration. This method modifies the traditional ABC scout-bee phase by employing the crossover and mutation operators of the DEO algorithm. To authenticate the performance and efficiency of the MHDORA-LBA technique, an experimental examination and comparative study are performed. Findings are shown on both GoCJ and synthetic data, and comparisons are made to the DRALBA or DRABC-LB procedures to see how successful the suggested method is. Based on the simulation findings, it is clear that the suggested strategy maximises throughput and resource utilisation while improving LB stability via task allocation that decreases response time and makespan.

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References

  1. Praveenchandar J, Tamilarasi A. An enhanced load balancing approach for dynamic resource allocation in cloud environments. Wirel Pers Commun. 2022;122(4):3757–76. https://doi.org/10.1007/s11277-021-09110-x.

    Article  Google Scholar 

  2. Belgacem A. Dynamic resource allocation in cloud computing: analysis and taxonomies. Computing. 2022;104(3):681–710. https://doi.org/10.1007/s00607-021-01045-2.

    Article  MathSciNet  Google Scholar 

  3. Sridevi S, Uthariaraj VR. Efficient load balancing and dynamic resource allocation in cloud environment. Int J Eng Res Technol. 2015;4(2):758–62.

    Google Scholar 

  4. Nabi S, Ibrahim M, Jimenez JM. DRALBA: dynamic and resource aware load balanced scheduling approach for cloud computing. IEEE Access. 2021. https://doi.org/10.1109/ACCESS.2021.3074145.

    Article  Google Scholar 

  5. Y. Mao, X. Chen, and X. Li, “Max–Min task scheduling algorithm for load balance in cloud computing,” Adv Intell Syst Comput, vol. 255, 2014, pp. 457–465. https://doi.org/10.1007/978-81-322-1759-6_53.

  6. Hussain A, Aleem M, Khan A, Iqbal MA, Islam MA. RALBA: a computation-aware load balancing scheduler for cloud computing. Cluster Comput. 2018;21(3):1667–80. https://doi.org/10.1007/s10586-018-2414-6.

    Article  Google Scholar 

  7. Mishra SK, Khan MA, Sahoo B, Puthal D, Obaidat MS, Hsiao KF. Time efficient dynamic threshold-based load balancing technique for Cloud Computing, in IEEE CITS 2017 - 2017 international conference on computer, information and telecommunication systems, 2017. pp. 161–165. https://doi.org/10.1109/CITS.2017.8035327.

  8. Nabi S, Ahmed M. OG-RADL: overall performance-based resource-aware dynamic load-balancer for deadline constrained Cloud tasks. J Supercomput. 2021;77(7):7476–508. https://doi.org/10.1007/s11227-020-03544-z.

    Article  Google Scholar 

  9. Akay B, Karaboga D. A modified artificial bee colony algorithm for real-parameter optimization. Inf Sci (Ny). 2012;192:120–42. https://doi.org/10.1016/j.ins.2010.07.015.

    Article  Google Scholar 

  10. Dasgupta K, Mandal B, Dutta P, Mandal JK, Dam S. A genetic algorithm (GA) based Load balancing strategy for cloud computing. Procedia Technol. 2013. https://doi.org/10.1016/j.protcy.2013.12.369.

    Article  Google Scholar 

  11. Alguliyev R, Imamverdiyev Y, Abdullayeva F. PSO-based load balancing method in cloud computing. Autom Control Comput Sci. 2019;53:45–55. https://doi.org/10.3103/S0146411619010024.

    Article  Google Scholar 

  12. Mishra R. Ant colony optimization: a solution of load balancing in cloud. Int J Web Semant Technol. 2012;3(2):33–50. https://doi.org/10.5121/ijwest.2012.3203.

    Article  Google Scholar 

  13. Fan Z, Shen H, Wu Y, Li Y. Simulated-annealing load balancing for resource allocation in cloud environments, in parallel and distributed computing, applications and technologies, PDCAT Proceedings, 2013. pp. 1–6. https://doi.org/10.1109/PDCAT.2013.7.

  14. L. Shen, J. Li, Y. Wu, Z. Tang, and Y. Wang, “Optimization of Artificial Bee Colony Algorithm Based Load Balancing in Smart Grid Cloud,” in 2019 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia), 2019. pp. 1131–1134. https://doi.org/10.1109/ISGT-Asia.2019.8881232.

  15. Karaboga D, Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim. 2007;39(3):459–71. https://doi.org/10.1007/s10898-007-9149-x.

    Article  MathSciNet  Google Scholar 

  16. Zhu G, Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput. 2010;217(7):3166–73. https://doi.org/10.1016/j.amc.2010.08.049.

    Article  MathSciNet  Google Scholar 

  17. Kang F, Li J, Li H. Artificial bee colony algorithm and pattern search hybridised for global optimization. Appl Soft Comput. 2013;13(4):1781–91. https://doi.org/10.1016/j.asoc.2012.12.025.

    Article  Google Scholar 

  18. Alatas B. Chaotic bee colony algorithms for global numerical optimization. Expert Syst Appl. 2010;37(8):5682–7. https://doi.org/10.1016/j.eswa.2010.02.042.

    Article  Google Scholar 

  19. Gao W, Liu S, Huang L. A global best artificial bee colony algorithm for global optimization. J Comput Appl Math. 2012;236(11):2741–53. https://doi.org/10.1016/j.cam.2012.01.013.

    Article  MathSciNet  Google Scholar 

  20. M. El-Abd, “Generalized opposition-based artificial bee colony algorithm,” in 2012 IEEE congress on evolutionary computation, 2012. pp. 1–4. https://doi.org/10.1109/CEC.2012.6252939.

  21. Li TL, Liu FA, Wang XH. Modified artificial bee colony algorithm based on divide-and-conquer strategy. Kongzhi yu Juece/Control Decis. 2015;30(2):316–20. https://doi.org/10.13195/j.kzyjc.2013.1442.

    Article  Google Scholar 

  22. Karaboga D, Gorkemli B, Ozturk C, Karaboga N. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev. 2014;42(1):21–57. https://doi.org/10.1007/s10462-012-9328-0.

    Article  Google Scholar 

  23. Ghumman NS, Kaur R. Dynamic combination of improved max-min and ant colony algorithm for load balancing in cloud system. In 6th international conference on computing, communications and networking technologies, ICCCNT 2015, 2016. https://doi.org/10.1109/ICCCNT.2015.7395172.

  24. Goyal A, Chahal NS. Bio inspired approach for load balancing to reduce energy consumption in cloud data center. In International conference communication, control and intelligent systems, CCIS 2015. 2016. https://doi.org/10.1109/CCIntelS.2015.7437950

  25. Handur Vidya S, Marakumbi Prakash R. Response time analysis of dynamic load balancing algorithms in cloud computing. In Proceedings of the world conference on smart trends in systems, security and sustainability, WS4 2020, 2020. https://doi.org/10.1109/WorldS450073.2020.9210305

  26. Prasanna Kumar KR, Gm S, Yamsani N, Kiran Kumar TM, Pani AK. A novel energy-efficient hybrid optimization algorithm for load balancing in cloud computing. In IEEE 1st international conference on ambient intelligence, knowledge informatics and industrial electronics, AIKIIE 2023, 2023. https://doi.org/10.1109/AIKIIE60097.2023.10390196

  27. Rani S, Kumar D, Dhingra S. A review on dynamic load balancing algorithms. In 3rd IEEE 2022 international conference on computing, communication, and intelligent systems, ICCCIS 2022. 2022. https://doi.org/10.1109/ICCCIS56430.2022.10037671.

  28. Surbhi O, Bhatt MC. Performance evaluation of load balancing algorithms in hadoop. 2018. https://doi.org/10.1109/iccmc.2018.8487916.

  29. Mohapatra S, Aryendu I, Panda A, Padhi AK. A modern approach for load balancing using forest optimization algorithm. In Proceedings of the 2nd international conference on computing methodologies and communication, ICCMC 2018, 2018. https://doi.org/10.1109/ICCMC.2018.8487765.

  30. Narwal A, Dhingra S. A novel approach for credit-based resource aware load balancing algorithm (CB-RALB-SA) for scheduling jobs in cloud computing. Data Knowl Eng. 2023. https://doi.org/10.1016/j.datak.2022.102138.

    Article  Google Scholar 

  31. Chen BR et al. FlowTele: Remotely Shaping Traffic on Internet-Scale Networks. In CoNEXT 2022 - Proceedings of the 18th international conference on emerging networking experiments and technologies. 2022. https://doi.org/10.1145/3555050.3569139.

  32. Kumar M, Sharma SC. Load balancing algorithm to minimize the makespan time in cloud environment. UK World J Model Simul. 2018;1(4):276–88.

    Google Scholar 

  33. Calheiros RN, Ranjan R, Beloglazov A, De Rose CAF, Buyya R. CloudSim: a toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw - Pract Exp. 2011;41(1):23–50. https://doi.org/10.1002/spe.995.

    Article  Google Scholar 

  34. Karaboga D. An idea based on honey bee swarm for numerical optimization, Technical Report - TR06. Tech. Report, Erciyes Univ., 2005.

  35. Bullinaria JA, AlYahya K. Artificial bee colony training of neural networks. In: Terrazas G, Otero FEB, Masegosa AD, editors. Nature inspired cooperative strategies for optimization (NICSO 2013): learning, optimization and interdisciplinary applications. Cham: Springer International Publishing; 2014. p. 191–201. https://doi.org/10.1007/978-3-319-01692-4_15.

    Chapter  Google Scholar 

  36. Suganthan PN. Differential evolution algorithm: recent advances. In: Dediu A-H, Martín-Vide C, Truthe B, editors. Theory and practice of natural computing. Berlin Heidelberg: Springer; 2012. p. 30–46.

    Chapter  Google Scholar 

  37. Das S, Suganthan PN. Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput. 2011;15(1):4–31. https://doi.org/10.1109/TEVC.2010.2059031.

    Article  Google Scholar 

  38. Hussain A, Aleem M. GoCJ: Google cloud jobs dataset for distributed and cloud computing infrastructures. Data. 2018. https://doi.org/10.3390/data3040038.

    Article  Google Scholar 

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Mishra, R., Gupta, M. MHDORA-LBA: Dynamic and Optimized Resource-Aware Load Balancing Approach for Resource Allocation. SN COMPUT. SCI. 5, 795 (2024). https://doi.org/10.1007/s42979-024-03162-z

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