Load balancing technique based on hybrid resource utilization in cloud computing

Vishal Awasthi; Ajay Tiwari; V Jayaram; Surendra Kumar Shukla; Kumud Pant; K S Abhilash

doi:10.53730/ijhs.v6nS5.9079

Authors

Vishal Awasthi Asst Prof, Department Of ECE, School Of Engineering (UIET), C.S.J.M. University, Kanpur
Ajay Tiwari Asst Prof, Department Of ECE, School Of Engineering (UIET), C.S.J.M. University, Kanpur
Jayaram V Research Scholar, Dept of Mechanical Engineering, Noorul Islam center for Higher Education Tamilnadu
Surendra Kumar Shukla Associate Professor, Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, 248002
Kumud Pant Designation: Associate Professor, Department of Biotechnology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India, 248002
Abhilash. KS Managing Director, EduCorp Centre for Research and Advanced Studies Pvt. Ltd. Thiruvananthapuram, Kerala

Keywords:

Cloud Computing, Load Balancing, Hybrid Resource Utilization, Dynamic Load Balancing, Virtual Machine

Abstract

Cloud computing uses the internet to supply dynamic services including memory, data, bandwidth and applications. Work schedules have an influence on cloud service reliability and performance. A proper provisioning method is required for a systematic resource allocation, which comprises of large virtual resources. Depending on the present state of the system, load balancing solutions can be distinguished as dynamic or static. Dynamic or static load balancing solutions can be employed to increase server response time or to raise load balancing factors for quicker and more efficient resource utilization. To decrease the load across resources and maximize CPU usage, a hybrid load balancing technique is developed. In the cloud, we have a finite quantity of resources that must be efficiently managed in order to fulfill tasks. Requests are transmitted to a cloud server, which assigns work via quadratic probing. During load balancing, the load is shifted from heavy-weighted servers to lighter-weighted servers, enhancing CPU usage. The suggested methodology's performance was assessed using average mean response time, make-span, average make-span, and average resource utilization. The Load Balancing Algorithm (LBA) is created with the primary purpose of reducing job completion time and increasing the average resource utilization ratio.

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References

G. Breiter and M. Behrendt, “Life Cycle and Characteristics of Services in the World of Cloud Computing,” IBM Journal of Research and Development, vol. 53, pp. 3:1–3:8, July 2009.

J. Liu, X.-G. Luo, X.-M. Zhang, F. Zhang, and B.-N. Li, “Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm,” International Journal of Computer Science Issues, vol. 10, p. 134, Jan. 2013.

Monika, D. P. Kumar, and D. S. Tyagi, “Survey on Various Scheduling Algorithmsin Cloud Computing,” International Journal of Scientific & Engineering Research, vol. 7, pp. 204–209, Dec. 2016.

R. Bansal and P. Gautam, “Extended Round RobinLoadBalancinginCloudComputing,” International Journal of Engineering and Computer Science,vol. 3, Aug. 2014.

M. Kumar and S. Sharma, “Dynamic Load Balancing Algorithm For Balancing The Workload Among Virtual Machine In Cloud Computing,” Procedia Computer Science, vol. 115, pp. 322–329, Dec. 2017.

P. Maheta and S. Dave, “Utilizing Round Robin Concept for Load Balancing Algorithm at Virtual Machine Level in Cloud Environment,” vol. 94, pp. 23–29, May 2014.

S. Kumar and S. Khurana, “Scheduling in Cloud Computing : A Review,” International Journal of Advanced Research in Computer Science, vol. 5, no. 1, pp. 79–81, 2014.

Saber, W., Moussa, W., Ghuniem, A. M., & Rizk, R. (2021). Hybrid load balance based on genetic algorithm in cloud environment. International Journal of Electrical & Computer Engineering (2088-8708), 11(3).

Annie Poornima Princess, G., & Radhamani, A. S. (2021). A hybrid meta-heuristic for optimal load balancing in cloud computing. Journal of Grid Computing, 19(2), 1-22.

Muteeh, A., Sardaraz, M., & Tahir, M. (2021). MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization. Cluster Computing, 24(4), 3135-3145.

Kodli, S., & Terdal, S. (2021). Hybrid max-min genetic algorithm for load balancing and task scheduling in cloud environment. Int J Intell Eng Syst., 14(1), 63-71.z

Gundu, S. R., & Anuradha, T. (2019). Improved hybrid algorithm approach based load balancing technique in cloud computing. Global journal of computer science and technology.

Kaur, A., & Kaur, B. (2019). Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment. Journal of King Saud University-Computer and Information Sciences.

Subalakshmi, S., & Malarvizhi, N. (2017). Enhanced hybrid approach for load balancing algorithms in cloud computing. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2(2), 136-142.

Lawanyashri, M., Balusamy, B., & Subha, S. (2017). Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications. Informatics in Medicine Unlocked, 8, 42-50.