Enhanced load aware weighted round robin algorithm in cloud

  • Authors

    • Thulasi Raman CHRIST (Deemed to be University)
    • Arokia Paul Rajan CHRIST (Deemed to be University)
    https://doi.org/10.14419/ijet.v7i4.21491

    Received date: October 10, 2018

    Accepted date: April 9, 2019

    Published date: April 21, 2019

  • Enhanced Load-Aware WRR, Dynamic Weighting, Virtual Machine, Cloud Analyst.

  • Abstract

    In a large-scale distributed cloud network there are lots of factors which affect the performance of the distributed systems, among this load balancing scheduling has a huge impact. It is recommendable to have a load balancer which equally splits the workload among all the available servers, according to the required parameters. The parameters of each server or a request can be termed as heavy loads or light loads relative to one another. Therefore, in the cloud environment, we need to assess the server capacity to overcome the high traffic and balance the loads properly. Subsequently, there is a need of a dynamic load balancing algorithm which splits and distribute all the loads equally on the basis of different parameters of the servers. The main aim of this research work is to address these requirements by devising a dynamic load aware load balancer for a heterogenous cloud environment. This is used to determine the weights of each queue dynamically based on the current traffic characteristics and static weights assigned to each server. The aim is to improve the average throughput and also to reduce the packet loss in the cloud networks. The experimentation of the proposed algorithm is performed using a simulator and the simulation results prove that there is a better improvement in the performance of the load balancer and also improves the average throughput compared with the existing WRR.

  • References

    1. Reena Panwar and Bhawna Mallick , “Load Balancing in Cloud Computing using Dynamic Load Management Algorithm”, 2015 In-ternational Conference on Green Computing and Internet of Things (ICGCIoT), https://doi.org/10.1109/ICGCIoT.2015.7380567.
    2. Rodrigo N. Calheiros, Rajiv Ranjan, Anton Beloglazov, Cesar A. F. De Rose and Rajkumar Buyya , “CloudSim: a toolkit for modelling and simulation of cloud computing environments and evaluation of resource provisioning algorithms”, Wiley Online Library in 2010.
    3. Muskan Garg and Rajnesh Narula, “Optimized Load Balancing in Cloud Computing using Hybrid Approach of Round Robin and Max-Min Scheduling”, International Journal of Science and Re-search, Volume 6 Issue 1, January 2017, ISSN (Online): 2319-7064.
    4. Brototi Mondal, Kousik Dasgupta and Paramartha Dutta, “Load Bal-ancing In Cloud Computing using Stochastic Hill Climbig-A Soft Computing Approach”, Procedia Techology-sciverse ScienceDirect, https://doi.org/10.1016/j.protcy.2012.05.128.
    5. Suman Rani, Vinod Saroha and Sanjeev Rana, “A Hybrid approach of Round Robin, Throttle & Equally Spaced Technique for Load Balancing in cloud environment”, International Journal of Innova-tions and Advancement in computer science, Volume 6, Issue 8, Au-gust 2017, ISSN 2347 – 8616.
    6. Ramesh Prajapati, Dushyantsinh and Samrat Khanna, “Comparison of static and dynamic load balancing in Grid Computing”, Interna-tional Journal for Technological Research in Engineering, Volume 2, Issue 7, March-2015, ISSN (Online): 2347 – 4718.
    7. Zenon Chaczko, Venkatesh Mahadevan, Shahrzad Aslanzadeh, and Christopher Mcdermid, “Availability and Load Balancing in Cloud Computing”, International Conference on computer and software modelling, IPCSIT vol. 14-2011.
    8. Ahmed Alsheikhy, Reda Ammar and Raafat Elfouly, “An Improved Dynamic Round Robin Scheduling Algorithm based on a variant quantum time”, International Journal of computer science, Engineer-ing and Information Technology, Vol 5, No.1 February 2016.
    9. Abdulaziz Alnowiser, Eman Aldhahri and Abdulrahman Alahmadi, “Enhanced Weighted Round Robin (EWRR) Scheduling with DVFS Technology in Cloud”, in Intenational Conference on Com-putational Sciene and Computational Intelligence, https://doi.org/10.1109/CSCI.2014.62.
    10. Vishwas Bagwaiys and Sandeep k. Raghuwanshi, “Hybrid Ap-proach using Throttled and ESCE load balancing algorithms in cloud computing”, International journal of web and semantic technology, April 2016, vol 3. Issue 2, P33, https://doi.org/10.1109/ICGCCEE.2014.6921418.
    11. R.Arokia Paul Rajan, “Service Request Scheduling based on Quanti-fication Principle using Conjoint Analysis and Z-score in Cloud”, In-ternational Journal of Electrical and Computer Engineering (IJECE), Vol. 8, No. 2, April 2018, pp . 1238~1246.
    12. Kalpana Ettikyala and Y. Rama Devi, “A Study on Cloud Simula-tion Tools”, International journal of computer applications, vol 115-No. 14, April 2015.
    13. Harlen Kaur and Er. Vinay Gautam, “A survey of various cloud simulators”, International journal of computer sciences and engineer-ing, vol-2, Issue-9, 2016.
    14. Jayaprakash Maltare and Balwant Prajapat, “Dynamic Load Balanc-ing in cloud computing using cloud sim”, International Journal of Computer applications, Vol 148-No.5, August 2016.
    15. Abhay Kumar Agarwal and Atul Raj, “A New Static Load Balanc-ing Algorithm in Cloud Computing”, International Journal of Com-puter Applications, Volume 132-No.2, December 2015.
    16. Seema Nagar and Ajeet KR Bhartee, “A comparative study on load balancing algorithms in cloud computing”, International journal of computer science trends and technology (IJCST), vol-3, Issue-4, Aug 2016.
    17. Sunita Rani Jindal and Sahil Vashist, “A sophisticated study of round robin and equally spread current execution in cloud computing”, In-ternational journal of advanced research in computer science and software engineering, vol-4, Issue-8, Aug 2014.

  • Downloads

  • How to Cite

    Raman, T., & Paul Rajan, A. (2019). Enhanced load aware weighted round robin algorithm in cloud. International Journal of Engineering and Technology, 7(4), 5777-5782. https://doi.org/10.14419/ijet.v7i4.21491