Load Balancing for Improved QoS in the Cloud ‎Computing

Authors

  • Ramin Goudarzi Karim Department of CIS
  • Fatemeh Rasoulpour Department of Computer Engineering, Institute of Higher Education, Tenekabon, Iran‎
  • Shamila Saeedi Department of Computer Engineering, Institute of Higher Education, Tenekabon, Iran‎

Keywords:

Cloud computing, Load balancing, Throughput, Scalability‎, Power consumption‎

Abstract

The emergence of cloud computing technology has led to the development of load-balancing algorithms. This paper presents the performance analysis of different load-balancing algorithms based on different metrics such as response time, processing time, scalability, throughput, system stability and power consumption. The primary purpose of this article is to help us propose a new algorithm by studying the behaviour of the various existing algorithms.                                                                                                                            

References

‎[1]‎ Tennakoon, D., Chowdhury, M., & Luan, T. H. (2023). Cloud-based load balancing using double Q-‎learning for improved Quality of Service. Wireless Netw 29, 1043–1050 (2023). https://doi.org/10.1007/s11276-‎‎018-1888-8.‎

‎[2]‎ Zhou, J., Lilhore, U. K., Hai, T., Simaiya, S., Jawawi, D. N. A., Alsekait, D., … Hamdi, M. (2023). ‎Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud ‎computing. Journal of cloud computing, 12, 85 (2023). https://doi.org/10.1186/s13677-023-00453-3.‎

‎[3] ‎Ghafir, S., Alam, M. A., Siddiqui, F., & Naaz, S. (2024). Load balancing in cloud computing via ‎intelligent PSO-based feedback controller. Sustainable computing: informatics and systems, 41, 100948.‎

‎[4]‎Abdelhafeez, A., & Aziz, A. S. (2024). Multi-criteria decision-making model for rank dtrategy to ‎overcome barriers to integrating the AI and cloud systems in the IT industry. Soft computing fusion with ‎applications, 1(1), 1–9.‎

‎[5]‎ Ghasemi, A., Isaai, M., Bandarian, R., & Ekhtiarzadeh, A. (2022). designing a qualitative model of the ‎micro foundations of dynamic capabilities in cloud computing service providers in Iran. Innovation ‎management and operational strategies, 3(2), 150–159.‎

‎[6]‎ Kumar, A., & Thomaz, A. C. F. (2022). Smart bus ticketing system through IoT enabled technology. Big ‎data and computing visions, 2(1), 1–8.‎

‎[7]‎ Muniz, R. D. F., Almaz Ali Yousif, B., & Shemshad, A. (2022). River water quality monitoring through ‎IoT enabled technologies. Computational algorithms and numerical dimensions, 1(1), 35–39.‎

‎[8]‎ Gulbaz, R., Siddiqui, A. B., Anjum, N., Alotaibi, A. A., Althobaiti, T., & Ramzan, N. (2021). Balancer ‎genetic algorithm—A novel task scheduling optimization approach in cloud computing. Applied sciences, ‎‎11(14), 6244.‎

‎[9]‎ Jyoti, A., & Shrimali, M. (2020). Dynamic provisioning of resources based on load balancing and service ‎broker policy in cloud computing. Cluster computing, 23(1), 377–395.‎

‎[10]‎ Mohammadian, V., Navimipour, N. J., Hosseinzadeh, M., & Darwesh, A. (2021). Fault-tolerant ‎load balancing in cloud computing: A systematic literature review. IEEE access, 10, 12714–12731.‎

‎[11]‎ Belgaum, M. R., Musa, S., Alam, M. M., & Su’ud, M. M. (2020). A systematic review of load ‎balancing techniques in software-defined networking. IEEE access, 8, 98612–98636.‎

‎[12]‎ Ullah, A., Nawi, N. M., & Khan, M. H. (2020). BAT algorithm used for load balancing purpose ‎in cloud computing: an overview. International journal of high performance computing and networking, 16(1), ‎‎43–54.‎

‎[13]‎ Sriram, G. S. (2022). Challenges of cloud compute load balancing algorithms. International ‎research journal of modernization in engineering technology and science, 4(1), 1186–1190.‎

‎[14]‎ Siddiqui, S., Darbari, M., & Yagyasen, D. (2020). An QPSL queuing model for load balancing in ‎cloud computing. International journal of e-collaboration (ijec), 16(3), 33–48.‎

‎[15]‎ Kumar, M., & Sharma, S. C. (2020). PSO-based novel resource scheduling technique to improve ‎QoS parameters in cloud computing. Neural computing and applications, 32(16), 12103–12126.‎

‎[16]‎ Talaat, F. M., Saraya, M. S., Saleh, A. I., Ali, H. A., & Ali, S. H. (2020). A load balancing and ‎optimization strategy (LBOS) using reinforcement learning in fog computing environment. Journal of ‎ambient intelligence and humanized computing, 11(11), 4951–4966.‎

‎[17]‎ Mohapatra, H., & Rath, A. K. (2019). Fault tolerance through energy balanced cluster formation ‎‎(ebcf) in wsn. Smart innovations in communication and computational sciences (pp. 313–321). Singapore: ‎Springer Singapore.‎

‎[18]‎ Panda, H., & Mohapatra, H. (2019). WSN based water channelization: an approach of smart water ‎‎[Thesis]. ‎

Published

2024-01-14

How to Cite

Load Balancing for Improved QoS in the Cloud ‎Computing. (2024). Smart City Insights, 1(1), 1-6. https://sci.reapress.com/journal/article/view/18