Load balancing techniques in cloud platform: A systematic study

Ranjit Rajak; Anjali Choudhary; Mohammad Sajid

doi:10.52756/ijerr.2023.v30.002

Authors

Ranjit Rajak Department of Computer Science and Applications, Dr. Harisingh Gour Central University, Sagar, Madhya Pradesh, India https://orcid.org/0000-0003-2746-3278
Anjali Choudhary Department of Computer Science and Applications, Dr. Harisingh Gour Central University, Sagar, Madhya Pradesh, India https://orcid.org/0000-0003-0273-4836
Mohammad Sajid Department of Computer Science, Aligarh Muslim University, Aligarh-202002, India https://orcid.org/0000-0001-8822-5332

DOI:

https://doi.org/10.52756/ijerr.2023.v30.002

Keywords:

Cloud computing, load balancing, makespan, response time, virtual machine

Abstract

In the current scenario, researchers have made a new invention and added to the computing paradigm every next second. Cloud computing is one of the most demanding, practical, accessible and extended technologies based on ‘pay as per use model’ and works on virtualisation via internet. Data sharing and accessing have become easy by properly organising various resources, such as storage, servers, development tools, software, etc, in cloud. Handling these resources has faced many challenges, such as cost management, system performance, migration, load imbalance, reliability and privacy etc. Load imbalance is one of the most important factors which are solved by load balancing techniques. This paper introduced the detailed classification of load balancing methods and techniques that are taken as a solution to overcome such problems and also helps future researchers. Also given is a proposed model for load balancing and some comparative studies of the heuristics methods based on platforms and simulator tools.

References

Alam, M. I., Pandey, M., & Rautaray, S. S. (2014). A proposal of resource allocation management for cloud computing. International Journal of Cloud Computing and Services Science, 3(2), 79.

Amanpreet, K., & Bikrampal, K. (2022). Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment. Journal of King Saud University- Computer and Information Sciences, 34(3), 813-824. https://doi.org/10.1016/j.jksuci.2019.02.010

Babbar, H., Rani, S., Masud, M., Verma, S., Anand, D., & Jhanjhi, N. (2021). Load balancing algorithm for migrating switches in software-defined vehicular networks. CMC-Comput Mater Continua, 67(1), 1301–1316. ttps://doi.org/10.32604/cmc.2021.014627

Babbar, H., Parthiban, S., Radhakrishnan, G., & Rani, S. (2022). A genetic load balancing algorithm to improve the QoS metrics for software defined networking for multimedia applications. Multimedia Tools and Applications, 81(7), 9111-9129. https://doi.org/10.1007/s11042-021-11467-x

Chaczko, Z., Mahadevan, V., Aslanzadeh, S., &Mcdermid, C. (2011). Availability and load balancing in cloud computing. IACSIT Press.In International Conference on Computer and Software Modeling, Singapore, 14, 134-140.

Chien, N. K., Son, N. H., & Loc, H. D. (2016). Load balancing algorithm based on estimating finish time of services in cloud computing. IEEE. In 2016 18th International Conference on Advanced Communication Technology (ICACT), pp. 228-233. https://doi.org/10.1109/ICACT.2016.7423340

Darji, V., Shah, J., & Mehta, R. (2014). Survey paper on various load balancing algorithms in cloud computing. International Journal of Scientific & Engineering Research, 5(5), 583-588.

Garg, S., Dwivedi, R. K., & Chauhan, H. (2015). Efficient utilization of virtual machines in cloud computing using Synchronized Throttled Load Balancing.IEEE. In 2015 1st International Conference on Next Generation Computing Technologies (NGCT), pp. 77-80. https://doi.org/10.1109/NGCT.2015.7375086

Haidri, R. A., Alam, M., Shahid, M., Prakash, S., & Sajid, M. (2022). A deadline aware load balancing strategy for cloud computing. Concurrency and Computation: Practice and Experience, 34(1), e6496. https://doi.org/10.1002/cpe.6496

Li, K., Xu, G., Zhao, G., Dong, Y., & Wang, D. (2011). Cloud task scheduling based on load balancing ant colony optimization. IEEE. In 2011 Sixth Annual China Grid Conference, pp. 3-9. https://doi.org/10.1109/ChinaGrid.2011.17

Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load balancing in cloud computing: a big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), 149-158. https://doi.org/10.1016/j.jksuci.2018.01.003

Mondal, B., & Choudhury, A. (2015). Simulated annealing (SA) based load balancing strategy for cloud computing. International Journal of Computer Science and Information Technologies, 6(4), 3307-3312.

Rajak, R. (2018). A comparative study: Taxonomy of high performance computing (HPC). International Journal of Electrical and Computer Engineering, 8(5), 3386-3391. https://doi.org/10.11591/ijece.v8i5.pp3386-3391

Rajak, N., & Rajak, R. (2021). Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform. Machine Learning Approach for Cloud Data Analytics in IoT, pp.195-226. https://doi.org/10.1002/9781119785873.ch9

Rong, W., & Bin, R. (2015). Research on the Cloud Computing Load Balance Degree of Priority Scheduling Algorithm based on Convex Optimization Theory. Atlantis Press.In 2015 Conference on Informatization in Education, Management and Business (IEMB-15), pp. 156-160. https://doi.org/10.2991/iemb-15.2015.31

Sajid, M., & Raza, Z. (2013). Cloud computing: Issues & challenges. In International Conference on Cloud, Big Data and Trust, 20(13),13-15. https://doi.org/10.1201/b16318-3

Sajid, M., & Raza, Z. (2016). Energy-aware stochastic scheduling model with precedence constraints on DVFS-enabled processors. Turkish Journal of Electrical Engineering and Computer Sciences, 24(5), 4117-4128. https://doi.org/10.3906/elk-1505-112

Shafiq, D. A., Jhanjhi, N. Z., & Abdullah, A. (2022). Load balancing techniques in cloud computing environment: A review. Journal of King Saud University-Computer and Information Sciences. 34(7), 3910-3933. https://doi.org/10.1016/j.jksuci.2021.02.007

Shahid, M., Raza, Z.,& Sajid, M. (2015). Level based batch scheduling strategy with idle slot reduction under DAG constraints for computational grid. Journal of Systems and Software, 108, 110-133. https://doi.org/10.1016/j.jss.2015.06.016

Sharma, S., Luhach, A.K., Abdhullah, S.S. (2016). An optimal load balancing technique for cloud computing environment using bat algorithm. Ind. J. Sci. Technol., 9(28), 1–4. https://doi.org/10.17485/ijst/2016/v9i28/98384

Sharma, S., & Sajid, M. (2021). Integrated fog and cloud computing issues and challenges. International Journal of Cloud Applications and Computing (IJCAC), 11(4), 174-193. https://doi.org/10.4018/IJCAC.2021100110

Sharma, V., & Sharma, H.C.A. (2021) Review of cloud computing scheduling algorithms. International Journal of Innovative Science & Research Technology, 6(12), 565-570.

Zhang, Q., Cheng, L., &Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Internet Services and Applications, 1, 7-18. https://doi.org/10.1007/s13174-010-0007-6

Zhu, Y., Zhao, D., Wang, W., & He, H. (2016). A novel load balancing algorithm based on improved particle swarm optimization in cloud computing environment. Springer International Publishing. In Human Centered Computing: Second International Conference, HCC 2016, Colombo, Sri Lanka, January 7-9, 2016, Revised Selected Papers 2. pp. 634-645. https://doi.org/10.1007/978-3-319-31854-7_57