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Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing

Received: 20 May 2019     Accepted: 31 July 2019     Published: 16 August 2019
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Abstract

Cloud computing consists of a cluster of computing resources that are delivered over a network, which is accomplished by utilizing virtualization technologies to consolidate and allocate resources suitable for various different software applications. Therefore, an efficient task scheduling in the cloud would be required to improve the performance of the cloud. In this paper, implementation of a model that seeks to improve load balancing algorithm for virtual machine load balancing was performed using simulations. A method by which average burst time was used as the time quantum for the round robin load balancing algorithm to achieve more effective time sharing. Results obtained from the simulations along with performance evaluation carried out shows response time and data center processing time achieved using the improved model is slightly minimal compared to the other algorithms. This shows more effective load balancing by achieving a better overall throughput.

Published in American Journal of Networks and Communications (Volume 8, Issue 2)
DOI 10.11648/j.ajnc.20190802.11
Page(s) 47-58
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Cloud Computing, Round Robin, Virtual Machine (VM), Load Balancing, Burst Time, Time Quantum, Response Time

References
[1] Abdulkarim, A., Boukari, S., Muhammed, I., & Ahhmed, F. A. (2018). An Improved Round Robin Load Balancing Algorithm in Cloud Computing Using Average Burst Time. International Journal of Scientific & Engineering Research, Volume 9, Isuue 3, 1495-1502.
[2] Aggarwal, R., & Gupta, L. (2017). Load Balancing in Cloud Computing. International Journal of Computer Science and Mobile Computing, Volume 6, Issue 6, 180-186.
[3] Bey, K. B., Benhammadi, F., & Benaissa, R. (2015). Balancing Heuristic for Independent Task Scheduling in Cloud Computing. 12th International Symposium on Programming and Systems, 7-12.
[4] Ettikyala, K., & Latha, Y. N. (2016). Rank Based Efficient Task Scheduling for Cloud Computing. IEEE International Conference on Data Mining and Advanced Computing, 343-346.
[5] Krishna, P. V. (2013). Honey Bee Behavior Inspired Load Balancing of Task in Cloud Computing Environments. Applied Software Computing, 2292-2303.
[6] Mathew, T., Sekaran, K. C., & Jose, J. (2014). Study and Analysis of Various Task Scheduling Algorithm in Cloud Computing Environment. International Conference on Advances in Computing, 658-664.
[7] Nusrat, P., Amit, A., & Ravi, R. (2014). Round Robin Approach for VM Load Balancing Algorithm in Cloud Computing Environment. Internation Journal of Advanced Research in Computer Science and Software Engineering, 34-39.
[8] Phi, N. X., & Hung, T. C. (2017). Load Balancing Algorithm to Improve Response Time on Cloud Environment. International Journal on Cloud Computing: Services and Architecture, Volume 7, No. 6, 1-12.
[9] Sachdeva, R., & Kakkar, S. (2017). A Novel Approach in Cloud Computing for Load Balancing Using Composite Algorithms. International Journal of Advanced Research in Computer Science and Software Engineering, Volume 7, Issue 2, 51-56.
[10] Selvarani, S., & Sadhasivam, G. S. (2010). Improved cost-based algorithm for task scheduling in cloud computing. Computational Intelligence and computing research, 1-5.
[11] Zannon, N., & Rawshdeh, D. (2015). STASR: A New Task Scheduling Algorithm for Cloud Environment. Network Protocols and Algorithms (pp. 81-95). Macrothink Institude.
[12] Rani, P., & Nagpal, P. (2017). Optimized Task Scheduling Algorithm for Cloud Computing Environment. International Journal of Emerging Trends & Technology in Computer Science, Volume 6, Issue 5, 39-47.
[13] George, M. S., Das, K. C. N., & Pushpa, B. R. (2017). Enhanced honeybee inspired load balancing algorithm for cloud environment. 2017 International Conference on Communication and Signal Processing (ICCSP). doi: 10.1109/iccsp.2017.8286670.
[14] Kumar, A. S.; Venkatesan, M. Task scheduling in a cloud computing environment using HGPSO algorithm. Cluster Comput. 2018, 1–7.
[15] Wang, S.; Qian, Z.; Yuan, J.; You, I. A DVFS based energy-e_cient tasks scheduling in a data center. IEEE Access 2017, 5, 13090–13102.
Cite This Article
  • APA Style

    Abdulrahman Abdulkarim, Ishaq Muhammed, Lele Mohammed, Abbas Babayaro. (2019). Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing. American Journal of Networks and Communications, 8(2), 47-58. https://doi.org/10.11648/j.ajnc.20190802.11

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    ACS Style

    Abdulrahman Abdulkarim; Ishaq Muhammed; Lele Mohammed; Abbas Babayaro. Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing. Am. J. Netw. Commun. 2019, 8(2), 47-58. doi: 10.11648/j.ajnc.20190802.11

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    AMA Style

    Abdulrahman Abdulkarim, Ishaq Muhammed, Lele Mohammed, Abbas Babayaro. Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing. Am J Netw Commun. 2019;8(2):47-58. doi: 10.11648/j.ajnc.20190802.11

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  • @article{10.11648/j.ajnc.20190802.11,
      author = {Abdulrahman Abdulkarim and Ishaq Muhammed and Lele Mohammed and Abbas Babayaro},
      title = {Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing},
      journal = {American Journal of Networks and Communications},
      volume = {8},
      number = {2},
      pages = {47-58},
      doi = {10.11648/j.ajnc.20190802.11},
      url = {https://doi.org/10.11648/j.ajnc.20190802.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20190802.11},
      abstract = {Cloud computing consists of a cluster of computing resources that are delivered over a network, which is accomplished by utilizing virtualization technologies to consolidate and allocate resources suitable for various different software applications. Therefore, an efficient task scheduling in the cloud would be required to improve the performance of the cloud. In this paper, implementation of a model that seeks to improve load balancing algorithm for virtual machine load balancing was performed using simulations. A method by which average burst time was used as the time quantum for the round robin load balancing algorithm to achieve more effective time sharing. Results obtained from the simulations along with performance evaluation carried out shows response time and data center processing time achieved using the improved model is slightly minimal compared to the other algorithms. This shows more effective load balancing by achieving a better overall throughput.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Performance Analysis of an Improved Load Balancing Algorithm in Cloud Computing
    AU  - Abdulrahman Abdulkarim
    AU  - Ishaq Muhammed
    AU  - Lele Mohammed
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    DO  - 10.11648/j.ajnc.20190802.11
    T2  - American Journal of Networks and Communications
    JF  - American Journal of Networks and Communications
    JO  - American Journal of Networks and Communications
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    EP  - 58
    PB  - Science Publishing Group
    SN  - 2326-8964
    UR  - https://doi.org/10.11648/j.ajnc.20190802.11
    AB  - Cloud computing consists of a cluster of computing resources that are delivered over a network, which is accomplished by utilizing virtualization technologies to consolidate and allocate resources suitable for various different software applications. Therefore, an efficient task scheduling in the cloud would be required to improve the performance of the cloud. In this paper, implementation of a model that seeks to improve load balancing algorithm for virtual machine load balancing was performed using simulations. A method by which average burst time was used as the time quantum for the round robin load balancing algorithm to achieve more effective time sharing. Results obtained from the simulations along with performance evaluation carried out shows response time and data center processing time achieved using the improved model is slightly minimal compared to the other algorithms. This shows more effective load balancing by achieving a better overall throughput.
    VL  - 8
    IS  - 2
    ER  - 

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Author Information
  • Department of Computer Science, Faculty of Science, Federal Polytechnic, Bauchi, Nigeria

  • Department of Computer Science, Faculty of Science, Federal Polytechnic, Bauchi, Nigeria

  • Department of Computer Science, Faculty of Science, Federal Polytechnic, Bauchi, Nigeria

  • Department of Information and Communication Technology, Bauchi State University Gadau, Bauchi, Nigeria

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