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International Journal of Wireless and Microwave Technologies(IJWMT)

ISSN: 2076-1449 (Print), ISSN: 2076-9539 (Online)

Published By: MECS Press

IJWMT Vol.9, No.6, Nov. 2019

Load Balancing in Cloud Computing Using Hungarian Algorithm

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Author(s)

Mohammad Irfan Bala, Mohammad Ahsan Chishti

Index Terms

Cloud Computing, CloudSim, Load balancing, Resource Scheduling, Virtual machine

Abstract

Cloud computing is a highly popular computing paradigm providing on-demand resources with high reliability and availability. The user requests are fulfilled by providing a virtual machine with the requested configuration. However, with the ever-increasing load on the cloud resources, the need for optimal resource utilization of the cloud resources has become the need of the hour. Load balancing has been identified as one of the possible ways to improve resource utilization in the cloud and the current state-of-the-art algorithms indicate the numerous attempts made to find the approximate solution for this NP-hard problem. In this work, we have focused on evaluating the efficiency of the Hungarian algorithm for load distribution in the cloud and compared its performance with First-come-first-serve (FCFS). The simulations were carried out in CloudSim and show remarkable improvement in various performance parameters. Finish time of a given task schedule was reduced by 41% and average execution time was reduced by 13% in the Hungarian algorithm when compared with FCFS.  The simulations were carried out under different workload conditions to validate our results.

Cite This Paper

Mohammad Irfan Bala, Mohammad Ahsan Chishti, " Load Balancing in Cloud Computing Using Hungarian Algorithm ", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.9, No.6, pp. 1-10, 2019.DOI: 10.5815/ijwmt.2019.06.01

Reference

[1]X. Chen, L. Jiao, W. Li, and X. Fu, “Efficient multi-user computation offloading for mobile-edge cloud computing,” IEEE/ACM Trans. on …, vol. 24, no. 5, pp. 2795–2808, 2016.

[2]M. A. Rodriguez and R. Buyya, “Deadline Based Resource Provisioning and Scheduling Algorithm for Scientific Workflows on Clouds,” IEEE Trans. Cloud Comput., 2014.

[3]M. I. Bala and M. A. Chishti, “Survey of applications, challenges and opportunities in fog computing,” Int. J. Pervasive Comput. Commun., vol. 15, no. 2, pp. 80–96, Jun. 2019.

[4]R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Softw. - Pract. Exp., 2011.

[5]P. Samal and P. Mishra, “Analysis of Variants in Round Robin Algorithms for Load Balancing in Cloud Computing,” Int. J. Comput. Sci. Inf. Technol., 2013.

[6]S. Ghosh and C. Banerjee, “Dynamic Time Quantum Priority Based Round Robin for Load Balancing In Cloud Environment,” in 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), 2018, pp. 33–37.

[7]F. Saeed, N. Javaid, M. Zubair, and M. Ismail, “Load Balancing on Cloud Analyst Using First Come First Serve Scheduling Algorithm,” no. June, 2019.

[8]M. Gahlawat, “Analysis and Performance Assessment of CPU Scheduling Algorithms in Cloud using Cloud Sim,” Int. J. Appl. Inf. Syst., vol. 5, no. 9, pp. 5–8, 2013.

[9]Z. Chenhong, Z. Shanshan, L. Qingfeng, X. Jian, and H. Jicheng, “Independent tasks scheduling based on genetic algorithm in cloud computing,” in Proceedings - 5th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2009, 2009.

[10]T. D. Braun et al., “A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems,” J. Parallel Distrib. Comput., 2001.

[11]Y. Mao, X. Chen, and X. Li, “Max–Min Task Scheduling Algorithm for Load Balance in Cloud Computing,” Springer, New Delhi, 2014, pp. 457–465.

[12]S. Parsa and R. Entezari-Maleki, “RASA: A New Task Scheduling Algorithm in Grid Environment,” World Appl. Sci. J., vol. 7, pp. 152–160, 2009.

[13]G. N. Gan, T. L. Huang, and S. Gao, “Genetic simulated annealing algorithm for task scheduling based on cloud computing environment,” in Proceedings - 2010 International Conference on Intelligent Computing and Integrated Systems, ICISS2010, 2010.

[14]A. E. keshk, A. B. El-Sisi, and M. A. Tawfeek, “Cloud Task Scheduling for Load Balancing based on Intelligent Strategy,” Int. J. Intell. Syst. Appl., vol. 6, no. 5, pp. 25–36, 2014.

[15]T. S. Rani, “Task Scheduling on Virtual Machines using BAT Strategy for Efficient Utilization of Resources in Cloud Environment,” vol. 12, no. 17, pp. 6663–6669, 2017.

[16]R. Kaur and K. S. Dhindsa, “Efficient Task Scheduling using Load Balancing in Cloud Computing,” Int. J. Adv. Netw. Appl., vol. 10, no. 03, pp. 3888–3892, 2018.

[17]A. Hota, S. Mohapatra, and S. Mohanty, “Survey of Different Load Balancing Approach-Based Algorithms in Cloud Computing: A Comprehensive Review,” in Computational Intelligence in Data Mining, 2019, pp. 99–110.

[18]D. Karaboga and B. Akay, “A comparative study of Artificial Bee Colony algorithm,” Appl. Math. Comput., 2009.

[19]K. Duan, S. Fong, S. Siu, W. Song, and S. Guan, “Adaptive Incremental Genetic Algorithm for Task Scheduling in Cloud Environments,” Symmetry (Basel)., vol. 10, no. 5, p. 168, May 2018.

[20]M. Berwal and C. Kant, “Load Balancing in Cloud Computing,” Int. J. Comput. Sci. Commun., vol. 6, pp. 52–58, 2015.

[21]D. A. Agarwal and S. Jain, “Efficient Optimal Algorithm of Task Scheduling in Cloud Computing Environment,” Int. J. Comput. Trends Technol., vol. 9, no. 7, pp. 344–349, 2014.

[22]K. Parikh, N. Hawanna, P. K. Haleema, R. Jayasubalakshmi, and N. C. S. N. Iyengar, “Virtual Machine Allocation Policy in Cloud Computing Using CloudSim in Java,” Int. J. Grid Distrib. Comput., vol. 8, no. 1, pp. 145–158, 2015.

[23]K. Dasgupta, B. Mandal, P. Dutta, J. K. Mandal, and S. Dam, “A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing,” Procedia Technol., 2013.

[24]A. Husseinzadeh Kashan, “League Championship Algorithm (LCA): An algorithm for global optimization inspired by sport championships,” Appl. Soft Comput. J., vol. 16, no. August, pp. 171–200, 2014.

[25]A. P. Xiong and C. X. Xu, “Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center,” Math. Probl. Eng., vol. 2014, 2014.