HYBRID LOAD BALANCING POLICY TO OPTIMIZE RESOURCE DISTRIBUTION AND RESPONSE TIME IN CLOUD ENVIRONMENT

Year 2022, , 101 - 109, 31.12.2022

Lencho M. Bokıye , Ilker Ali Ozkan

https://doi.org/10.18100/ijamec.1158866

Abstract

Load balancing and task scheduling are the main challenges in Cloud Computing. Existing load balancing algorithms have a drawback in considering the capacity of virtual machines while distributing loads among them. The proposed algorithm works toward solving existing issues, such as fair load distribution, avoiding underloading and overloading, and improving response time. It implements best practices of Throttled load balancing algorithm and Equally Shared Current Execution algorithm. Virtual machines are selected based on the ratio of their bandwidth and load allocation count. Requests are sent to a Virtual Machine with higher bandwidth and lower load allocation count. Proposed algorithm checks for the availability of VM based on their capacity. This process is performed by selecting two VMs and comparing their vmWeight capacity. The one with the least vmWeight is selected. CloudAnalyst is used for simulation, response time evaluation, and resource utilization evaluation. The simulation result of the proposed algorithm is compared with three well-known load-balancing algorithms. These are Round Robin, Throttled Load balancing algorithm, and Enhanced Active Monitoring. Load-balancing Proposed Algorithm selects VMs based on their Algorithm. The proposed algorithm has improved over other algorithms in load distribution, response time, and resource utilization. All virtual machines in the data centers are loaded with a relatively equal number of tasks according to their capacity. This resulted in fair resource sharing and load distribution.

Keywords

Cloud Analyst, Cloud Computing, Load Balancing, Optimization, Resource Utilization, Response time

References

• [1] S. S. Sefati, M. Mousavinasab, and R. Zareh Farkhady, “Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: performance evaluation,” J. Supercomput., vol. 78, no. 1, pp. 18–42, Jan. 2022, doi: 10.1007/S11227-021-03810-8/FIGURES/14.
• [2] M. Gopala and K. Sriram, “CHALLENGES OF CLOUD COMPUTE LOAD BALANCING ALGORITHMS,” Accessed: Jun. 12, 2022. [Online]. Available: www.irjmets.com.
• [3] N. Kannan, Y. Mobarak, and F. Alharbi, “Application of cloud computing for economic load dispatch and unit commitment computations of the power system network,” Adv. Intell. Syst. Comput., vol. 1108 AISC, pp. 1179–1189, 2020, doi: 10.1007/978-3-030-37218-7_124.
• [4] S. M. G. Kashikolaei, A. A. R. Hosseinabadi, B. Saemi, M. B. Shareh, A. K. Sangaiah, and G. Bin Bian, “An enhancement of task scheduling in cloud computing based on imperialist competitive algorithm and firefly algorithm,” J. Supercomput., vol. 76, no. 8, pp. 6302–6329, 2020, doi: 10.1007/s11227-019-02816-7.
• [5] D. A. Shafiq, N. Z. Jhanjhi, and A. Abdullah, “Load balancing techniques in cloud computing environment: A review,” J. King Saud Univ. - Comput. Inf. Sci., Mar. 2021, doi: 10.1016/J.JKSUCI.2021.02.007.
• [6] S. Afzal and G. Kavitha, “Load balancing in cloud computing – A hierarchical taxonomical classification,” J. Cloud Comput., vol. 8, no. 1, 2019, doi: 10.1186/s13677-019-0146-7.
• [7] M. Nanjappan and P. Albert, “Hybrid-based novel approach for resource scheduling using MCFCM and PSO in cloud computing environment,” Concurr. Comput. Pract. Exp., vol. 34, no. 7, p. e5517, Mar. 2022, doi: 10.1002/CPE.5517.
• [8] V. Richhariya, R. Dubey, and R. Siddiqui, “ORIENTAL JOURNAL OF Hybrid Approach for Load Balancing in Cloud Computing,” Orient. J. Comput. Sci. Technol., vol. Vol. 8, no. 3, pp. 241–246, 2015.
• [9] A. Alkhatib, S. Alzu’bi, A. A. Alkhatib, and T. Sawalha, “Load Balancing Techniques in Software-Defined Cloud Computing: an overview,” ieeexplore.ieee.org, doi: 10.1109/SDS49854.2020.9143874.
• [10] J. Bhatia, T. Patel, … H. T.-… S. on C., and undefined 2012, “HTV dynamic load balancing algorithm for virtual machine instances in cloud,” ieeexplore.ieee.org, Accessed: Feb. 12, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/6481229/?casa_token=P_h-elG6PQ0AAAAA:EYSjp67iqG1oju7hnbsUFRjB3WR9a9Xutyg4aHQ07PgzjUwxYB9yUrbaZzd3WeJ9x-jDhTJZBA.
• [11] B. H. Shanthan and L. Arockiam, “Resource Based Load Balanced Min Min Algorithm(RBLMM) for static Meta task Scheduling in Cloud,” Accessed: Jun. 13, 2022. [Online]. Available: http://www.ijetjournal.org.
• [12] S. Eswaran and M. Rajakannu, “Multiservice Load Balancing with Hybrid Particle Swarm Optimization in Cloud-Based Multimedia Storage System with QoS Provision,” Mob. Networks Appl., vol. 22, no. 4, pp. 760–770, Aug. 2017, doi: 10.1007/S11036-017-0840-Y/FIGURES/5.
• [13] G. Ramadhan, T. W. Purboyo, R. Latuconsina, and A. R. Robin, “Experimental Model for Load Balancing in Cloud Computing Using Throttled Algorithm,” Int. J. Appl. Eng. Res., vol. 13, no. 2, pp. 1139–1143, 2018, [Online]. Available: https://www.ripublication.com/ijaer18/ijaerv13n2_42.pdf.
• [14] A. Alkhatib, S. Alzu’bi, A. A. Alkhatib, A. Alsabbagh, R. Maraqa, and S. Alzubi, “Load balancing techniques in cloud computing: Extensive review,” researchgate.net, vol. 6, no. 2, pp. 860–870, 2021, doi: 10.25046/aj060299.
• [15] R. Somani and J. Ojha, “A Hybrid Approach for VM Load Balancing in Cloud Using CloudSim,” Int. J. Sci. Eng. Technol. Res., vol. 3, no. 6, pp. 1734–1739, 2014.
• [16] D. Patel, “International Journal of Modern Trends in Engineering and Research Efficient Throttled Load Balancing Algorithm in Cloud Environment,” Int. J. Mod. Trends Eng. Res., vol. 02, no. 03, pp. 463–481, 2015.
• [17] K. Kaur, “Equally Spread Current Execution Load Algorithm - A Novel Approach for Improving Data Centre ’ s Performance in Cloud Computing,” Int. J. Futur. Revolut. Comput. Sci. Commun. Eng., vol. Volume: 4, no. August, pp. 10–12, 2018.
• [18] S. A. Narale and P. K. Butey, “Throttled Load Balancing Scheduling Policy Assist to Reduce Grand Total Cost and Data Center Processing Time in Cloud Environment Using Cloud Analyst,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2018, no. Icicct, pp. 1464–1467, 2018, doi: 10.1109/ICICCT.2018.8473062.
• [19] M. U. Sapra1 and A. Sharma2, “Wisely Randomized Weighted Throttled Load-Balancing Algorithm for Cloud,” ijerm.in, vol. 7, no. 5, 2020, Accessed: Feb. 22, 2022. [Online]. Available: https://www.ijerm.in/tmp/Vol_7/IJERM_11070.pdf.
• [20] R. S. Sajjan, “Load Balancing and its Algorithms in Cloud Computing : A Survey International Journal of Computer Sciences and Engineering Open Access Load Balancing and its Algorithms in Cloud Computing : A Survey,” no. January 2017, 2019.
• [21] A. Singh, S. Bhat, R. Raju, R. D.-A. Comput, and undefined 2017, “Survey on various load balancing techniques in cloud computing,” academia.edu, vol. 7, no. 2, pp. 28–34, 2017, doi: 10.5923/j.ac.20170702.04.
• [22] H. Chandra, H. B.-I. J. of Computer, and undefined 2017, “A survey of load balancing algorithms in cloud computing,” researchgate.net, Accessed: Feb. 22, 2022. [Online]. Available: https://www.researchgate.net/profile/Harish-Sharma-5/publication/357458654_A_SURVEY_OF_LOAD_BALANCING_ALGORITHMS_IN_CLOUD_COMPUTING/links/61cf19dfb6b5667157ba8c08/A-SURVEY-OF-LOAD-BALANCING-ALGORITHMS-IN-CLOUD-COMPUTING.pdf.
• [23] S. Garg, D. V. Gupta, and R. K. Dwivedi, “Enhanced Active Monitoring Load Balancing algorithm for Virtual Machines in cloud computing,” Proc. 5th Int. Conf. Syst. Model. Adv. Res. Trends, SMART 2016, pp. 339–344, Apr. 2017, doi: 10.1109/SYSMART.2016.7894546.
• [24] A. F. Pathan and Sneha.N, “Simulation of Load Balancing Algorithms using Cloud Analyst,” Int. J. Eng. Res. Technol., vol. 2, no. 13, Jul. 2018, doi: 10.17577/IJERTCONV2IS13084.
• [25] J. Gustedt, E. Jeannot, and M. Quinson, “EXPERIMENTAL METHODOLOGIES FOR LARGE-SCALE SYSTEMS: A SURVEY,” http://dx.doi.org/10.1142/S0129626409000304, vol. 19, no. 3, pp. 399–418, Nov. 2011, doi: 10.1142/S0129626409000304.
• [26] 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., vol. 41, no. 1, pp. 23–50, Jan. 2011, doi: 10.1002/spe.995.
• [27] B. Wickremasinghe, R. N. Calheiros, and R. Buyya, “CloudAnalyst: A cloudsim-based visual modeller for analysing cloud computing environments and applications,” in Proceedings - International Conference on Advanced Information Networking and Applications, AINA, 2010, pp. 446–452, doi: 10.1109/AINA.2010.32.
• [28] 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., vol. 41, no. 1, pp. 23–50, Jan. 2011, doi: 10.1002/spe.995.
• [29] A. Pradhan and S. K. Bisoy, “A novel load balancing technique for cloud computing platform based on PSO,” J. King Saud Univ. - Comput. Inf. Sci., Oct. 2020, doi: 10.1016/J.JKSUCI.2020.10.016.
• [30] N. Mansouri, R. Ghafari, and B. M. H. Zade, “Cloud computing simulators: A comprehensive review,” Simul. Model. Pract. Theory, vol. 104, no. February, 2020, doi: 10.1016/j.simpat.2020.102144.