Enhancing Load Balancing in Cloud Computing using a Hybrid Ant Earthworm Optimization Algorithm
Keywords:
Antlion optimization algorithm, earthworm optimization algorithm, load balancing, VMAbstract
Information technology has significantly advanced since cloud computing was introduced. The distribution of resources from a data center involves many different factors, including the load balancing of workloads in the cloud environment. Through efficient and equitable job distribution among computing resources, load balancing increases user happiness and boosts system efficiency. Additionally, it would be challenging to maintain load balancing among resources because they are typically dispersed in a heterogeneous manner. Therefore, in this paper, a hybrid Ant earthworm optimization algorithm (AEOA) is implemented to perform effective load balancing. The earthworm optimization algorithm (EOA) was used as a local search approach to boost the Antlion optimization algorithm's (ALO) exploitation potential and prevent it from becoming stuck in local optima. Through load balancing between the VMs, this hybridization improves the machine's performance. Optimizing the waiting time of jobs will increase the throughput of VMs and preserve the balance between task priorities. The robustness of the algorithm will be validated by comparing the results of the proposed approach obtained from the simulation process with the existing load balancing and scheduling algorithm. All these experiments will be implemented on cloudsim
Downloads
References
Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)
Sajid, M., Raza, Z.: Cloud computing: issues challenges. In: International Conference on Cloud, Big Data and Trust, vol. 20, no. 13, pp. 13–15 (2013)
Kaur, P., Kaur, P.D.: Efficient and enhanced load balancing algorithms in cloud computing. Int. J. Grid Distrib. Comput. 8(2), 9–14 (2015)
Haryani, N., Jagli, D.:Dynamic method for load balancing in cloud computing. IOSR J. Comput. Eng. 16(4), 23–28 (2014)
Nishant, K., Sharma, P., Krishna, V., Gupta, C., Singh, K. P., & Rastogi, R. (2012, March). Load balancing of nodes in cloud using ant colony optimization. In 2012 UKSim 14th international conference on computer modelling and simulation (pp. 3-8). IEEE.
Pasupuleti, V., & Balaswamy, C. (2021). Performance analysis of fractional earthworm optimization algorithm for optimal routing in wireless sensor networks. EAI Endorsed Transactions on Scalable Information Systems, 8(32).
Sheng, J., et al. (2022). Learning to schedule multi-NUMA virtual machines via reinforcement learning. Pattern Recognition, 121, 108254. https:// doi. org/ 10. 1016/j. patcog. 2021. 108254
Mustafa, m. e. (2017). Load balancing algorithms round-robin (rr), least connection, and least loaded efficiency. Computer science & telecommunications, 51(1).
Yang, C. C., Chen, C., & Chen, J. Y. (2009, December). Random early detection web servers for dynamic load balancing. In 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks (pp. 364-368). IEEE.
Galloway, J. M., Smith, K. L., & Vrbsky, S. S. (2011, October). Power aware load balancing for cloud computing. In proceedings of the world congress on engineering and computer science (Vol. 1, pp. 19-21).
Pradhan, A., Bisoy, S. K., & Das, A. (2021). A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment. Journal of King Saud University - Computer and Information Sciences. https:// doi. org/ 10. 1016/j. jksuci. 2021. 01. 003
B. Shankar and P. Mishra, Cloud Computing for Optimization: Foundations, Applications, and Challenges.2018.
Benblidia MA, Brik B, Merghem-Boulahia L, Esseghir M (2019) Ranking fog nodes for tasks scheduling in fog-cloud environments: A fuzzy logic approach. In: 2019 15th international wireless communications & mobile computing conference (IWCMC). IEEE, pp 1451–1457
Ghanavati S, Abawajy J, Izadi D (2020) Automata-based dynamic fault tolerant task scheduling approach in fog computing. IEEE Trans Emerg Top Comput 6:66
Sun Y, Lin F, Xu H (2018) Multi-objective optimization of resource scheduling in fog computing using an improved nsga-ii. WirelPers Commun 102(2):1369–1385
Cho, K.M., Tsai, P.W., Tsai, C.W., Yang, C.S.: A hybrid metaheuristic algorithm forVMschedulingwith load balancing in cloud computing. Neural Comput. Appl. 26(6), 1297–1309 (2015)
Dam, S., Mandal, G., Dasgupta, K., Dutta, P.: Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing. In: IEEE Third International conference on Computer,Communication, Control and Information Technology (C3IT), pp. 1–7 (2015)
Priyadarsini, R.J., Arockiam, L.: Performance evaluation of min-min and max-min algorithms for job scheduling in federated cloud.Int. J. Comput. Appl. (0975–8887) 99(18), 47–54 (2014)
Kaur,R., Kinger, S.: Analysis of job scheduling algorithms in cloud computing. Int. J. Comput. Trends Technol. 9(7), 379–386 (2014)
Selvi, V., Umarani, D.R.: Comparative analysis of ant colony and particle swarm optimization techniques. Int. J. Comput. Appl.(0975– 8887) 5(4) (2010)
Downloads
Published
How to Cite
Issue
Section
License
![Creative Commons License](http://i.creativecommons.org/l/by-sa/4.0/88x31.png)
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.