LOAD BALANCING IN CLOUD COMPUTING VIA MAYFLY OPTIMIZATION ALGORITHM
DOI:
https://doi.org/10.59277/RRST-EE.2024.1.14Keywords:
Cloud computing, Load balancing, Mayfly optimization, Task schedulingAbstract
Cloud computing is a new technology that enables users to store and retrieve data via the Internet on demand rather than using their hardware. Cloud computing comprises distinct data centers (servers) and clients (users). Load unbalancing is a multi-variant, multi-constraint issue that lowers the efficacy and performance of system resources. Therefore, a load scheduling technique is needed to distribute work among the right VMs and preserve the trade-off between them. To achieve better performance, this paper presents a novel mayfly optimization algorithm for load balancing (MFO-LB), which utilizes mayfly flight behavior and mating dynamics. The proposed technique balances the load in the cloud by managing the incoming loads by allocating resources according to user requests. The proposed work intends to increase performance by uniformly dividing the workload among the virtual machines, which will decrease utilization and reaction time. The proposed MFO-LB approach is beneficial for maintaining system stability, reducing response time (RT), and maximizing resource productivity in cloud environments. Finally, the effectiveness of the proposed technique is assessed by employing several metrics, including execution cost, RT, execution time, and makespan. The proposed method achieves up to 23.4 % low RT, a 24 %decrease in makespan, and a 31.5 % decrease in completion time, respectively.
References
S. Afzal, G. Kavitha, Optimization of task migration cost in infrastructure cloud computing using IMDLB algorithm, In 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), pp. 1-6 (2018).
S. Afzal, G. Kavitha, Load balancing in cloud computing–A hierarchical taxonomical classification, Journal of Cloud Computing, 8, 1, pp. 1-24 (2019).
A. Ahilan, P. Deepa, A reconfigurable virtual architecture for memory scrubbers (VAMS) for SRAM based FPGA’s, Int. J. Appl. Eng. Res, 10, 10, pp. 9643-9648 (2015).
M. Alouane, H. El Bakkali, Virtualization in Cloud Computing: NoHype vs HyperWall new approach, In 2016 International Conference on Electrical and Information Technologies (ICEIT), pp. 49-54 (2016).
S. Alshattnawi, M. Al-Marie, Spider monkey optimization algorithm for load balancing in cloud computing environments, Int. Arab J. Inf. Technol., 18, 5, pp. 730-738 (2021).
G. Annie Poornima Princess, A. S. Radhamani, A hybrid meta-heuristic for optimal load balancing in cloud computing, Journal of Grid Computing, 19, 2, pp. 1-22 (2021).
V. M. Arul Xavier, S. Annadurai, Chaotic social spider algorithm for load balance aware task scheduling in cloud computing, Cluster Computing, 22, 1, pp. 287-297 (2019).
K. Balaji, Load balancing in cloud computing: issues and challenges, Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12, 2, pp. 3077-3084 (2021).
A. Chawla, N. S. Ghumman, Package-based approach for load balancing in cloud computing, In Big Data Analytics, pp. 71-77 (2019).
F. Ebadifard, S. M. Babamir, Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment, Cluster Computing, 24, 2, pp. 1075-1101 (2021).
M. Gamal, R. Rizk, H. Mahdi, B. E. Elnaghi, Osmotic bio-inspired load balancing algorithm in cloud computing, IEEE Access, 7, pp. 42735-42744 (2019).
N. Jafari Navimipour, F. Sharifi Milani, A comprehensive study of the resource discovery techniques in peer-to-peer networks, Peer-to-Peer networking and applications, 8, 3, pp. 474-492 (205).
B. Jana, M. Chakraborty, T. Mandal, A task scheduling technique based on particle swarm optimization algorithm in cloud environment, In Soft computing: theories and applications, Springer, Singapore, pp. 525-536 (2019).
A. Kaur, B. Kaur, P. Singh, M. S. Devgan, H. K. Toor, Load balancing optimization based on deep learning approach in cloud environment, International Journal of Information Technology and Computer Science, 12, 3, pp. 8-18 (2020).
N. Malarvizhi, J. Aswini, S. Sasikala, M. H. Chakravarthy, E. A. Neeba, Multi-parameter optimization for load balancing with effective task scheduling and resource sharing, Journal of Ambient Intelligence and Humanized Computing, pp. 1-9 (202).
S. T. Milan, L. Rajabion, H. Ranjbar, N. J. Navimipour, Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments, Computers & Operations Research, 110, pp. 159-187 (2019).
K. Mishra, S. K. Majhi, A binary bird swarm optimization based load balancing algorithm for cloud computing environment, Open Computer Science, 11, 1, pp. 146-160 (2021).
S. K. Mishra, B. Sahoo, P. P. Parida, Load balancing in cloud computing: a big picture, Journal of King Saud University-Computer and Information Sciences, 32, 2, pp. 149-158 (2020).
G. Muthusamy, S. R. Chandran, Cluster-based task scheduling using K-means clustering for load balancing in cloud datacenters, Journal of Internet Technology, 22, 1, pp. 121-130 (2021).
V. Polepally, K. Shahu Chatrapati, Dragonfly optimization and constraint measure-based load balancing in cloud computing, Cluster Computing, 22, 1, pp. 1099-1111 (2019).
H. Ren, Y. Lan, C. Yin, The load balancing algorithm in cloud computing environment, In Proceedings of 2012 2nd International Conference on Computer Science and Network Technology, pp. 925-928 (2012).
D. A. Shafiq, N. Z. Jhanjhi, A. Abdullah, M. A. Alzain, A load balancing algorithm for the data centres to optimize cloud computing applications, IEEE Access, 9, pp. 41731-41744 (2021).
A. K. Sharma, K. Upreti, B. Vargis, Experimental performance analysis of load balancing of tasks using honey bee inspired algorithm for resource allocation in cloud environment, Materials Today: Proceedings, (2020).
S. G. Sophia, K. K. Thanammal, An Improved Homomorphic Encryption Technology for the surveillance of cloud data, Solid State Technology, 63, 2s, pp. 2671-2674 (2020).
A. Ullah, Artificial bee colony algorithm used for load balancing in cloud computing, IAES International Journal of Artificial Intelligence, 8, 2, p. 156 (2019).
Y. Zhu, P. Liu, Multi-Dimensional Constrained Cloud Computing Task Scheduling Mechanism Based on Genetic Algorithm, International Journal of Online Engineering, 9, pp. 15-18 (2013).
S. Ziyath, S. Senthilkumar, MHO: meta heuristic optimization applied task scheduling with load balancing technique for cloud infrastructure services, Journal of Ambient Intelligence and Humanized Computing, 12, 6, pp. 6629-6638 (2021).
Downloads
Published
Issue
Section
License
Copyright (c) 2024 REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
