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Lateral Wolf Based Particle Swarm Optimization (LW-PSO) for Load Balancing on Cloud Computing

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Abstract

Cloud services are rapidly evolving and has become a demand. Consequently, Load Balancing (LB) is needed to enhance the use of resources by optimal distribution of workload among various Virtual Machines (VMs). This study intends to solve the task scheduling issues and provide optimal LB to all the VMs by implementing the proposed hybrid Lateral Wolf and Particle Swarm Optimization (LW-PSO). The study aims to find the optimized VMs by the proposed hybrid methodology. It also intends to perform parallel task scheduling, thereby minimizing the response time and afford results quickly for each of the assigned tasks. The study uses Lateral Wolf (LW) to perform task scheduling in a parallel way and the Particle Swarm Optimization (PSO) obtains the optimal solution based on LW so as to find the optimized VMs. This creates flexibility among the VMs as they are neither overloaded nor under-loaded. All the VMs are equally assigned tasks. The proposed LW finds the Fitness Value (FV) and save this value. Then, it is fed to PSO and the best particle is updated along with its position and velocity. This process helps to find the optimized VMs and assign loads in accordance with the obtained optimal solution. The performance analysis is carried out by considering significant parameters such as average load, processor utilization, and average turnaround time, average response time, runtime and memory utilization. The analytical results show that the proposed method performs effectively than the existing system with respect to the mentioned parameters.

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References

  1. Hota, A., Mohapatra, S., & Mohanty, S. (2019). Survey of different load balancing approach-based algorithms in cloud computing: A comprehensive review. Computational Intelligence in Data Mining. https://doi.org/10.1007/978-981-10-8055-5_10

    Article  Google Scholar 

  2. Gabi, D., Ismail, A. S., Zainal, A., & Zakaria Z. (2017). Solving task scheduling problem in cloud computing environment using orthogonal taguchi-cat algorithm. International Journal of Electrical & Computer Engineering (2088–8708), 7(3).

  3. Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load balancing in cloud computing: A big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), 149–158. https://doi.org/10.1016/j.jksuci.2018.01.003

    Article  Google Scholar 

  4. Gamal, M., Rizk, R., Mahdi, H., & Elnaghi, B. E. (2019). Osmotic bio-inspired load balancing algorithm in cloud computing. IEEE Access, 7, 42735–42744. https://doi.org/10.1109/ACCESS.2019.2907615

    Article  Google Scholar 

  5. Thakur, A., & Goraya, M. S. (2017). A taxonomic survey on load balancing in cloud. Journal of Network and Computer Applications, 98, 43–57. https://doi.org/10.1016/j.jnca.2017.08.020

    Article  Google Scholar 

  6. Upadhyay, S. K., Bhattacharya, A., Arya, S., & Singh, T. (2018). Load optimization in cloud computing using clustering: A survey. International Research Journal of Engineering and Technology, 5(4), 2455–2459.

    Google Scholar 

  7. Subalakshmi, S., & Malarvizhi, N. (2017). Enhanced hybrid approach for load balancing algorithms in cloud computing. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2(2), 136–142.

    Google Scholar 

  8. Saleh, H., Nashaat, H., Saber, W., & Harb, H. M. (2018). IPSO task scheduling algorithm for large scale data in cloud computing environment. IEEE Access, 7, 5412–5420. https://doi.org/10.1109/ACCESS.2018.2890067

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Jena, U., Das, P., & Kabat, M. (2020). Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2020.01.012

    Article  Google Scholar 

  11. Ebadifard, F., & Babamir, S. M. (2018). A PSO-based task scheduling algorithm improved using a load-balancing technique for the cloud computing environment. Concurrency and Computation: Practice and Experience., 30(12), e4368. https://doi.org/10.1002/cpe.4368

    Article  Google Scholar 

  12. Priya, V., Kumar, C. S., & Kannan, R. (2019). Resource scheduling algorithm with load balancing for cloud service provisioning. Applied Soft Computing, 76, 416–424. https://doi.org/10.1016/j.asoc.2018.12.021

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Lu, Y., & Sun, N. (2019). An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment. Cluster Computing, 22(1), 513–520.

    Article  MathSciNet  Google Scholar 

  15. Pourghaffari, A., Barari, M., & Sedighian, K. S. (2019). An efficient method for allocating resources in a cloud computing environment with a load balancing approach. Concurrency and Computation: Practice and Experience, 31(17), e5285. https://doi.org/10.1002/cpe.5285

    Article  Google Scholar 

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

    Google Scholar 

  17. Ahmad, M. O., & Khan, R. Z. (2019). Pso-based task scheduling algorithm using adaptive load balancing approach for cloud computing environment. International Journal of Scientific & Technology Research, 8(11).

  18. Devi, T. D., Subramani, A., & Anitha, P. (2021). Modified adaptive neuro fuzzy inference system based load balancing for virtual machine with security in cloud computing environment. Journal of Ambient Intelligence and Humanized Computing, 12(3), 3869–3876.

    Article  Google Scholar 

  19. Lawanyashri, M., Balusamy, B., & Subha, S. (2017). Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications. Informatics in Medicine Unlocked., 8, 42–50. https://doi.org/10.1016/j.imu.2017.02.005

    Article  Google Scholar 

  20. Hasan, R. A., & Mohammed, M. N. (2017). A krill herd behaviour inspired load balancing of tasks in cloud computing. Studies in Informatics and Control, 26(4), 413–424. https://doi.org/10.24846/v26i4y201705

    Article  Google Scholar 

  21. Zhou, Z., Li, F., Zhu, H., Xie, H., Abawajy, J. H., & Chowdhury, M. U. (2020). An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments. Neural Computing and Applications., 32(6), 1531–1541. https://doi.org/10.1007/s00521-019-04119-7

    Article  Google Scholar 

  22. Ebadifard, F., & Babamir, S. M. (2020). Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment. Cluster Computing. https://doi.org/10.1007/s10586-020-03177-0

    Article  Google Scholar 

  23. Prakash, S. (2018). A literature review of QoS with load balancing in cloud computing environment. Big Data Analytics 667–75.

  24. Haidri, R. A., Katti, C. P., & Saxena, P. C. (2019). Capacity based deadline aware dynamic load balancing (CPDALB) model in cloud computing environment. International Journal of Computers and Applications. https://doi.org/10.1080/1206212x.2019.1640932

    Article  Google Scholar 

  25. Sekaran, K., & Krishna, P. V. (2017). Cross region load balancing of tasks using region-based rerouting of loads in cloud computing environment. International Journal of Advanced Intelligence Paradigms, 9(5–6), 589–603. https://doi.org/10.1504/ijaip.2017.088151

    Article  Google Scholar 

  26. Jafarnejad Ghomi, E., Rahmani, A. M., & Qader, N. N. (2019). Service load balancing, scheduling, and logistics optimization in cloud manufacturing by using genetic algorithm. Concurrency and Computation: Practice and Experience, 31(20), e5329. https://doi.org/10.1002/cpe.5329

    Article  Google Scholar 

  27. Alla, H. B., Alla, S. B., Touhafi, A., & Ezzati, A. (2018). A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment. Cluster Computing, 21(4), 1797–1820.

    Article  Google Scholar 

  28. Devaraj, A. F. S., Elhoseny, M., Dhanasekaran, S., Lydia, E. L., & Shankar, K. (2020). Hybridization of firefly and improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments. Journal of Parallel and Distributed Computing., 142, 36–45. https://doi.org/10.1016/j.jpdc.2020.03.022

    Article  Google Scholar 

  29. Pradhan, A., Bisoy, S. K., & Das, A. (2021). A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment. Journal of King Saud University-Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2021.01.003

    Article  Google Scholar 

  30. Karunakaran, V. (2019). A stochastic development of cloud computing based task scheduling ALGORITHM. Journal of Soft Computing Paradigm (JSCP), 1(01), 41–48. https://doi.org/10.36548/jscp.2019.1.005

    Article  Google Scholar 

  31. Suresh, A., & Varatharajan, R. (2019). Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Computing, 22(5), 11039–11046. https://doi.org/10.1007/s10586-017-1293-6

    Article  Google Scholar 

  32. Xingjun, L., Zhiwei, S., Hongping, C., & Mohammed, B. O. (2020). A new fuzzy-based method for load balancing in the cloud-based Internet of things using a grey wolf optimization algorithm. International Journal of Communication Systems, 33(8), e4370. https://doi.org/10.1002/dac.4370

    Article  Google Scholar 

  33. Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Computing 1–19. https://doi.org/10.1007/s10586-020-03075-5

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This research work was not funded by any organization/institute/agency.

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I Am MEENA MALIK Hereby State That The Manuscript Title Entitled “Lateral Wolf Based Particle Swarm Optimization (LW-PSO) For Load Balancing On Cloud Computing” Submitted To Wireless Personal Communications, I and my Co-author Suman Confirm That This Work Is Original And Has Not Been Published Elsewhere, Nor Is It Currently Under Consideration For Publication Elsewhere. And I Am Assistant Professor in Computer Science Department, Chandigarh University, Mohali.

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Correspondence to Meena Malik.

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Malik, M., Suman Lateral Wolf Based Particle Swarm Optimization (LW-PSO) for Load Balancing on Cloud Computing. Wireless Pers Commun 125, 1125–1144 (2022). https://doi.org/10.1007/s11277-022-09592-3

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