Efficient Load Balancing and Optimal Resource Allocation Using Max-Min Heuristic Approach and Enhanced Ant Colony Optimization Algorithm over Cloud Computing
Keywords:
Cloud computing, Max-Min Heuristic (MMH) and Enhanced Ant Colony Optimization (EACO) algorithm, load balancing, resource allocationAbstract
The paradigm of virtualization technology, which underpins cloud computing, has lately become one of the most prominent concepts in the information technology (IT) sector. Virtualization is a technology which helps the users to access the cloud services. In the existing system, the resource allocation is not ensured and in few cases, speed of the process is reduced due to convergence issues. Hence, the performance of cloud computing as a whole has greatly declined. The Max-Min Heuristic (MMH) and Enhanced Ant Colony Optimisation (EACO) algorithms are introduced in this study to enhance load balancing and optimum resource allocation on the cloud to address the aforementioned difficulties. The suggested system comprises four primary stages, including cost-effective Virtual Machine (VM) migration, load balancing, and resource allocation. First, think about how many resources, tasks, virtual machines, and cloud users there are in cloud computing. This study uses the MMH method for load balancing, which equalizes the overall workloads throughout the cloud. By moving tasks from overloaded nodes to under loaded nodes, load balancing is accomplished. Following that, the EACO algorithm is utilized to allocate resources in a way that effectively chooses more optimum resources. In order to effectively fulfil Quality of Service (QoS) standards, it is utilized to choose the best resources for the relevant cloud needs. Increasing throughput and VM performance in the cloud, as well as lowering costs, are other key objectives. Finally, a cost-effective VM migration technique is used, which is based on the Weighted Support Vector Machine (WSVM) algorithm. With the use of SVM weight values, it is designed to identify the pattern of overload and underload. It also finds VM migration strategies that consume the least amount of energy while maintaining high service standards. The simulation results show that, compared to the current approaches, the proposed MMH+EACO algorithm performs better thanks to increased throughput and reduced computational complexity, cost complexity, Mean Square Error (MSE) rate, and energy usage.
Downloads
References
Potluri, Sirisha, and Katta Subba Rao. "Quality of service based task scheduling algorithms in cloud computing." International Journal of Electrical and Computer Engineering 7.2 (2017): 1088.
Tsai, Jinn-Tsong, Jia-Cen Fang, and Jyh-Horng Chou. "Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm." Computers & Operations Research 40.12 (2013): 3045-3055.’
Ding, D., Fan, X., Zhao, Y., Kang, K., Yin, Q., & Zeng, J. (2020). Q-learning based dynamic task scheduling for energy-efficient cloud computing. Future Generation Computer Systems, 108, 361-371.
Yakubu, Ismail Zahraddeen, et al. "Service level agreement violation preventive task scheduling for quality of service delivery in cloud computing environment." Procedia Computer Science 178 (2020): 375-385.
Gamal, Marwa, et al. "Osmotic bio-inspired load balancing algorithm in cloud computing." IEEE Access 7 (2019): 42735-42744.
Ebadifard, Fatemeh, Seyed Morteza Babamir, and Sedighe Barani. "A dynamic task scheduling algorithm improved by load balancing in cloud computing." 2020 6th International Conference on Web Research (ICWR). IEEE, 2020
Xiao, Zhen, Weijia Song, and Qi Chen. "Dynamic resource allocation using virtual machines for cloud computing environment." IEEE transactions on parallel and distributed systems 24.6 (2012): 1107-1117.
Zhang, Zhenzhong, et al. "Mvmotion: a metadata based virtual machine migration in cloud." Cluster Computing 17.2 (2014): 441-452
Li, Xiao-Ke, et al. "Virtual machine placement strategy based on discrete firefly algorithm in cloud environments." 2015 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2015
Zhixue, W. U. "Advances on virtualization technology of cloud computing." Journal of Computer Applications 37.4 (2017): 915
Gong, Siqian, et al. "Adaptive multivariable control for multiple resource allocation of service-based systems in cloud computing." IEEE Access 7 (2019): 13817-13831
Lin, Miao, et al. "Ant colony algorithm for multi-objective optimization of container-based microservice scheduling in cloud." IEEE access 7 (2019): 83088-83100
Su, Yingying, Zhichao Bai, and Dongbing Xie. "The optimizing resource allocation and task scheduling based on cloud computing and Ant Colony Optimization Algorithm." Journal of Ambient Intelligence and Humanized Computing (2021): 1-9.
Annadanam, Chakravarthy Sudarshan, Sudhakar Chapram, and T. Ramesh. "Intermediate node selection for Scatter-Gather VM migration in cloud data center." Engineering Science and Technology, an International Journal 23.5 (2020): 989-997
Maipan-Uku, J. Y., et al. "Max-average: An extended max-min scheduling algorithm for grid computing environtment." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 8.6 (2016): 43-47
Peng, Huijun, et al. "An improved feature selection algorithm based on ant colony optimization." IEEE Access 6 (2018): 69203-69209.
Paniri, Mohsen, Mohammad Bagher Dowlatshahi, and Hossein Nezamabadi-Pour. "MLACO: A multi-label feature selection algorithm
D’Agostino, Daniele, et al. "Combining edge and cloud computing for low-power, cost-effective metagenomics analysis." Future Generation Computer Systems 90 (2019): 79-85.
Singh, Yashaswi, Farah Kandah, and Weiyi Zhang. "A secured cost-effective multi-cloud storage in cloud computing." 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS). IEEE, 2011.
Rustam, Zuherman, Jacub Pandelaki, and Arga Siahaan. "Kernel spherical k-means and support vector machine for acute sinusitis classification." IOP Conference Series: Materials Science and Engineering. Vol. 546. No. 5. IOP Publishing, 2019
Elshabka, Mohamed A., et al. "Security-aware dynamic VM consolidation." Egyptian Informatics Journal (2020)
Arularasan, A. N. ., Aarthi, E. ., Hemanth, S. V. ., Rajkumar, N. ., & Kalaichelvi, T. . (2023). Secure Digital Information Forward Using Highly Developed AES Techniques in Cloud Computing. International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 122–128. https://doi.org/10.17762/ijritcc.v11i4s.6315
Sherje, D. N. . (2021). Content Based Image Retrieval Based on Feature Extraction and Classification Using Deep Learning Techniques. Research Journal of Computer Systems and Engineering, 2(1), 16:22. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/14
Kshirsagar, P. R., Reddy, D. H., Dhingra, M., Dhabliya, D., & Gupta, A. (2022). A review on application of deep learning in natural language processing. Paper presented at the Proceedings of 5th International Conference on Contemporary Computing and Informatics, IC3I 2022, 1834-1840. doi:10.1109/IC3I56241.2022.10073309 Retrieved from www.scopus.com
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
