Performance Evaluation of Load Balancing Algorithm for Virtual Machine in Data Centre in Cloud Computing

  • Authors

    • Yuganes A/P Parmesivan
    • Sazlinah Hasan
    • Abdullah Muhammed
    2018-12-09
    https://doi.org/10.14419/ijet.v7i4.31.23717
  • Cloud computing, Honey Bee (HB), Particle Swarm Optimization (PSO), Hybrid, Response time
  • Abstract

    Cloud computing has become biggest buzz in the computer era these days. It runs entire operating systems on the cloud and do everything on cloud to store data off-site. Cloud computing is primarily based on grid computing, but it’s a new computational model. Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services. The primary substance of cloud computing is to deal the computing power, storage, different sort of stages and services which assigned to the external users on demand through the internet. Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing. In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example, when cloud clients try to access and send request to the same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers. Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded. A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time. The proposed hybrid algorithm has been experimented by using CloudSim simulator. The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm.

     

     

  • References

    1. [1] Al-Rayis, E., & Kurdi, H. (2013). Performance Analysis of load balancing Architectures in Cloud computing. In Modelling Symposium (EMS), European (pp. 520-524). IEEE.

      [2] Issawi, S. F., Al Halees, A., & Radi, M. (2015). An Efficient Adaptive Load Balancing Algorithm for Cloud Computing Under Bursty Workloads. Engineering, Technology & Applied Science Research, 5(3), 795-800.

      [3] Ayesta, U., Erausquin, M., Ferreira, E., & Jacko, P. (2016). Optimal dynamic resource allocation to prevent defaults. Operations Research Letters, 44(4), 451-456.

      [4] Alamin, M.A., K.Elbashir, M., & A. Osman, A. (2017). A Load Balancing Algorithm to Enhance the Response Time in Cloud Computing. Red Sea University Journal of Basic and Applied Science, 473-490.

      [5] You, L., Peng, J., Chen, M., & Qiu, M. (2017). A Strategy to Improve the Efficiency of I/O Intensive Application in Cloud Computing Environment. Journal of Signal Processing Systems, 86(2-3), 149-156.

      [6] Xu, M., Tian, W., & Buyya, R. (2017). A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurrency and Computation: Practice and Experience, 29(12).

      [7] Singh, H., & Gangwar, R. C. (2014). Comparative Study of Load Balancing Algorithms in Cloud Environment. International Journal on Recent and Innovation Trends in Computing and Communication, Vol: 2, 3195- 3199.

      [8] Nema, L., Sharma, A., & Jain, S. (2016). Load Balancing Algorithm in Cloud Computing: An Entensive Survey. International Journal of Engineering Science and Computing, 7463-7468.

      [9] Rajeshkannan, R., & Aramudhan, M. (2016). Comparative study of Load Balancing Algorithms in cloud computing environment. Indian Journal of Science and Technology, 9(20).

      [10] Thakur, P., & Mahajan, M. (2017). Different Scheduling Algorithm in Cloud Computing: A Survey. International Journal of Modern Computer Science (IJMCS), 44-50.

      [11] Randles, M., Lamb, D., & Taleb-Bendiab, A. (2010). A comparative study into distributed load balancing algorithms for cloud computing. In Advanced Information Networking and Applications Workshops (WAINA), IEEE 24th International Conference on (pp. 551-556). IEEE.

      [12] Dave, A., Patel, B., & Bhatt, G. (2016, October). Load balancing in cloud computing using optimization techniques: A study. In Communication and Electronics Systems (ICCES), International Conference on (pp. 1-6). IEEE.

      [13] Rajoriya, S. (2014). Load Balancing Techniques in Cloud Computing: An Overview. International Journal of Science and Research, 3.

      [14] Nema, L., Sharma, A., & Jain, S. (2016). Load balancing algorithms in cloud computing: An extensive survey. International Journal of Engineering Science and Computing, 6(6).

      [15] Kaur, A., & Kaur, B. (2016). Load balancing in tasks using honey bee behavior algorithm in cloud computing. In Wireless Networks and Embedded Systems (WECON), 5th International Conference on (pp. 1-5). IEEE.

      [16] Mousavi, S., Mosavi, A., Varkonyi-Koczy, A. R., & Fazekas, G. (2017). Dynamic resource allocation in cloud computing. Acta Polytechnica Hungarica, 14(4).

      [17] Gudise, V. G., and Venayagamoorthy, G. K. (2003), Comparison of particle swarm optimization and backpropagation as training algorithms for neural networks, in Swarm Intelligence Symposium, Proceedings of the IEEE, pp. 110-117.

      [18] Hashem, W., Nashaat, H., & Rizk, R. (2017). Honey Bee Based Load Balancing in Cloud Computing. KSII Transactions on Internet and Information Systems (TIIS), 11(12), 5694-5711.

      [19] Awad, A. I., El-Hefnawy, N. A., & Abdel_kader, H. M. (2015). Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Computer Science, 65, 920-929.

      [20] Pakize, S. R., Khademi, S. M. & Gandomi , A. (2014). Comparison of CloudSim, CloudAnalyst And CloudReports Simulator in Cloud Computing. International journal of Computer Science & Network Solutions, 2(5), 19-27.

      [21] Ramos, V., Muge, F., & Pina, P. (2002). Self-Organized Data and Image Retrieval as a Consequence of Inter-Dynamic Synergistic Relationships in Artificial Ant Colonies. HIS, 87, 500-512.

      [22] Mousavi, S. M., & Gábor, F. (2016). A novel algorithm for Load Balancing using HBA and ACO in Cloud Computing environment. International Journal of Computer Science and Information Security, 14(6), 48.

      [23] Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.

      [24] Abdullah, M., & Othman, M. (2014). Simulated annealing approach to cost-based multi-quality of service job scheduling in cloud computing enviroment. American Journal of Applied Sciences, 11(6), 872.

  • Downloads

  • How to Cite

    A/P Parmesivan, Y., Hasan, S., & Muhammed, A. (2018). Performance Evaluation of Load Balancing Algorithm for Virtual Machine in Data Centre in Cloud Computing. International Journal of Engineering & Technology, 7(4.31), 386-390. https://doi.org/10.14419/ijet.v7i4.31.23717

    Received date: 2018-12-12

    Accepted date: 2018-12-12

    Published date: 2018-12-09