Load Balancing of Cloud Computing Service Model Empowered with Fuzzy Logic

Authors

  • Syed Atir Raza Shirazi Minhaj University
  • Abdul Hannan Khan School of Information Technology,Minhaj Uiversity,Lahore,Pakistan.
  • Shahid Rasool
  • Aqsa Anwar
  • Muhammad Ammar

DOI:

https://doi.org/10.33317/ssurj.500

Keywords:

Fuzzy logic system, Load Balancing, Quality of Services, Virtual Machine, CLB-PME-FES

Abstract

Nowadays, cloud computing is a growing field in research and the marketplace, including virtualization, internet service, computer software, and web services. A cloud comprises multiple elements like a consumer's data center and servers. It provides high availability, efficiency, scalability, mobility, fault tolerance, decreased overhead of users, reduced expenses of ownership, on-demand services, etc. A Variety Of users need various Quality of Service (QoS) demands. Therefore, the cloud supplier needs to arrange the tasks to get maximum advantages regarding their services, and the consumer's quality of service demands are satisfied. Currently, the need for cloud is increasing day by day, people moved towards cloud simultaneously, so its scale is up that's why the service providers are required to deal with enormous requests. The biggest challenge is the availability of services and maintaining the performance equivalent or more effective whenever workload occurs. Multiple requests are processed simultaneously; that's why the workload increased. The load balancer uses to resolve that issue. Our research shows how to balance the load in a cloud environment using fuzzy logic. The processor speed, storage capacity, and assigned load of Virtual Machine (VM) utilize to balance the load in cloud computing through fuzzy logic to achieve better processing time and storage utilization.

References

Alam, T. (2020). Cloud Computing and its role in Information Technology. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 1(2), 108-115.

Diaby, T., & Rad, B. B. (2017). Cloud computing: a review of the concepts and deployment models. International Journal of Information Technology and Computer Science, 9(6), 50-58.

Wang, J., Yang, Y., Wang, T., Sherratt, R. S., & Zhang, J. (2020). Big data service architecture: a survey. Journal of Internet Technology, 21(2), 393-405.

Saadat, A., & Masehian, E. (2019, December). Load balancing in cloud computing using genetic algorithm and fuzzy logic. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1435-1440).

Deepa, T., & Cheelu, D. (2017, August). A comparative study of static and dynamic load balancing algorithms in cloud computing. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3375-3378).

Dašić, P., Dašić, J., & Crvenković, B. (2016). Service models for cloud computing: Search as a service (SaaS). International Journal of Engineering and Technology (IJET), 8(5), 2366-2373.

Rashid, A., & Chaturvedi, A. (2019). Cloud computing characteristics and services: a brief review. International Journal of Computer Sciences and Engineering, 7(2), 421-426.

Yasrab, R. (2018). Platform-as-a-Service (PaaS): The Next Hype of Cloud Computing. arXiv preprint arXiv:1804.10811.

Xu, P., He, G., Li, Z., & Zhang, Z. (2018). An efficient load balancing algorithm for virtual machine allocation based on ant colony optimization. International Journal of Distributed Sensor Networks, 14(12).

Tong, R., & Zhu, X. (2010, November). A load balancing strategy based on the combination of static and dynamic. In 2010 2nd International Workshop on Database Technology and Applications(pp. 1-4).

Khiyaita, A., El Bakkali, H., Zbakh, M., & El Kettani, D. (2012). Load balancing cloud computing: state of art. 2012 National Days of Network Security and Systems, 106-109.

Pradhan, A., & Bisoy, S. K. (2020). A novel load balancing technique for cloud computing platform based on PSO. Journal of King Saud University-Computer and Information Sciences.

Kumar, M., & Sharma, S. C. (2020). Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. International Journal of Computers and Applications, 42(1), 108-117.

Swathy, R., Vinayagasundaram, B., Rajesh, G., Nayyar, A., Abouhawwash, M., & Abu Elsoud, M. (2020). Game theoretical approach for load balancing using SGMLB model in cloud environment. PloS one, 15(4).

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.

Singh, A., Korupolu, M., & Mohapatra, D. (2008, November). Server-storage virtualization: integration and load balancing in data centers. In SC'08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing (pp. 1-12). IEEE

Issawi, S. F., Al Halees, A., & Radi, M. (2015). An efficient adaptive load balancing algorithm for cloud computing under Bursty workloads. Engineering, Technology & Applied Science Research, 5(3), 795-800.

Panwar, P., Kaushal, C., Singla, A., & Rattan, V. (2022). Load Balancing in Multiprocessor Systems Using Modified Real-Coded Genetic Algorithm. In Innovations in Computational Intelligence and Computer Vision (pp. 201-210). Springer, Singapore.

Rathore, R., SHARMA, V., & GOLA, K. (2014). A new approach for load balancing in cloud computing. International Journal of Engineering and Computer Science, 2(5), 1636-1640.

Ragmani, A., Elomri, A., Abghour, N., Moussaid, K., & Rida, M. (2019). An improved hybrid fuzzy-ant colony algorithm applied to load balancing in cloud computing environment. Procedia Computer Science, 151, 519-526.

Moura, B., Schneider, G., Yamin, A., Pilla, M., & Reiser, R. (2019, August). Type-2 fuzzy logic approach for overloaded hosts in consolidation of virtual machines in cloud computing. In 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019) (pp. 668-675).

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Published

2023-06-28

How to Cite

Shirazi, S. A. R., Khan, A. H., Rasool, S., Anwar, A., & Ammar, M. (2023). Load Balancing of Cloud Computing Service Model Empowered with Fuzzy Logic. Sir Syed University Research Journal of Engineering & Technology, 13(1), 10–16. https://doi.org/10.33317/ssurj.500