MULTI-OBJECTIVE-OPTIMIZATION APPROACH FOR OPTIMAL TASK SCHEDUL-ING THROUGH IN DELAY SENSITIVE CLOUD ENVIRONMENT
DOI:
https://doi.org/10.7494/csci.2026.27.1.6526Abstract
Optimizing task scheduling in cloud computing is a major challenge that impacts system performance and resource use. Balancing different workloads, given the limits of the system and user needs, is difficult. Poor management of both underused and overburdened states can lead to problems like high energy use and hardware failures. Therefore, distributing tasks among virtual machines (VMs) is crucial in cloud task scheduling. This work introduces a dynamic load balancing algorithm called CHHO (Cuckoo Harris Hawk multi-objective Optimization). CHHO is a new hybrid method that combines Cuckoo Search Optimization (CSO) and Harris Hawk Optimization (HHO). This combination uses the strengths of both algorithms to address the complex issues of cloud task scheduling. Specifically, CHHO uses Cuckoo Search Optimization to widen the search area of Harris Hawk Optimization, aiming to improve factors such as cost, response time, and resource use. The CHHO algorithm is designed to improve system performance by increasing VM throughput, effectively distributing workloads across VMs, and maintaining a balance among task priorities through dynamic adjustments in task waiting times. To test the performance of CHHO, the algorithm is implemented in the CloudSim simulation environment. It is compared with existing load balancing algorithms on various performance measures. Our simulation results clearly show that CHHO performs better than existing algorithms, providing a strong and efficient solution for load-balancing in cloud computing. Introducing CHHO offers a significant advancement in the field, providing a dynamic and adaptable approach that improves cloud task scheduling and enhances the overall efficiency and effectiveness of cloud computing systems.
Downloads
References
[1] Adaikalaraj J.R., et al.: Load Balancing In Cloud Computing Environment Us-
ing Quasi Oppositional Dragonfly Algorithm, Turkish Journal of Computer and
Mathematics Education (TURCOMAT), vol. 12(10), pp. 3256–3273, 2021.
[2] Adhikari M., Nandy S., Amgoth T.: Meta heuristic-based task deployment mech-
anism for load balancing in IaaS cloud, Journal of Network and Computer Ap-
plications, vol. 128, pp. 64–77, 2019. doi: 10.1016/j.jnca.2018.12.010.
[3] Annie Poornima Princess G., Radhamani A.: A hybrid meta-heuristic for optimal
load balancing in cloud computing, Journal of grid computing, vol. 19(2), p. 21,
2021. doi: 10.1007/s10723-021-09560-4.
[4] Balaji K., Kiran P.S., Kumar M.S.: WITHDRAWN: An energy efficient load
balancing on cloud computing using adaptive cat swarm optimization, 2021.
doi: 10.1016/j.matpr.2020.11.106.
[5] Ebadifard F., Babamir S.M., Barani S.: A dynamic task scheduling algorithm
improved by load balancing in cloud computing. In: 2020 6th International
Conference on Web Research (ICWR), pp. 177–183, IEEE, 2020. doi: 10.1109/
icwr49608.2020.9122287.
[6] Ejaz H., Awan M.A., Muzzammil H., Ullah M., Akhavan-Safar A., daSilva
L., Tanveer A.: Strength improvement/optimization methods in adhesively
bonded joints: A comprehensive review of past and present techniques, Me-
chanics of Advanced Materials and Structures, pp. 1–29, 2024. doi: 10.1080/
15376494.2024.2400610.
[7] Farrag A.A.S., Mohamad S.A., El Sayed M.: Swarm Intelligent Algorithms for
solving load balancing in cloud computing, Egyptian Computer Science Journal,
vol. 43(1), pp. 45–57, 2019.
[8] Golchi M.M., Saraeian S., Heydari M.: A hybrid of firefly and improved
particle swarm optimization algorithms for load balancing in cloud environ-
ments: Performance evaluation, Computer Networks, vol. 162, p. 106860, 2019.
doi: 10.1016/j.comnet.2019.106860.
[9] Haris M., Khan R.Z.: A systematic review on cloud computing, International
Journal of Computer Sciences and Engineering, vol. 6(11), pp. 632–639, 2018.
doi: 10.26438/ijcse/v6i11.632639.
[10] Haris M., Khan R.Z.: A systematic review on load balancing issues in cloud
computing, Sustainable Communication Networks and Application: ICSCN 2019,
pp. 297–303, 2020.
[11] Haris M., Zubair S.: Mantaray modified multi-objective Harris hawk optimization
algorithm expedites optimal load balancing in cloud computing, Journal of King
Saud University-Computer and Information Sciences, vol. 34(10), pp. 9696–9709,
2022. doi: 10.1016/j.jksuci.2021.12.003.
[12] Hou X., Zhao G.: Resource scheduling and load balancing fusion algorithm
with deep learning based on cloud computing, International Journal of Infor-
mation Technology and Web Engineering (IJITWE), vol. 13(3), pp. 54–72, 2018.
doi: 10.4018/978-1-7998-0414-7.ch058.
[13] Jena U.K., Das P.K., Kabat M.R.: Hybridization of meta-heuristic algorithm
for load balancing in cloud computing environment, Journal of King Saud
University-Computer and Information Sciences, vol. 34(6), pp. 2332–2342, 2022.
doi: 10.1016/j.jksuci.2020.01.012
[14] Junaid M., Sohail A., Ahmed A., Baz A., Khan I.A., Alhakami H.: A hybrid
model for load balancing in cloud using file type formatting, IEEE Access, vol. 8,
pp. 118135–118155, 2020. doi: 10.1109/access.2020.3003825.
[15] Junaid M., Sohail A., Rais R.N.B., Ahmed A., Khalid O., Khan I.A., Hussain
S.S., Ejaz N.: Modeling an optimized approach for load balancing in cloud, IEEE
access, vol. 8, pp. 173208–173226, 2020. doi: 10.1109/access.2020.3024113.
[16] Kaur A., Kaur B.: Load balancing optimization based on hybrid Heuristic-
Metaheuristic techniques in cloud environment, Journal of King Saud University-
Computer and Information Sciences, vol. 34(3), pp. 813–824, 2022. doi: 10.1016/
j.jksuci.2019.02.010.
[17] Kaur A., Narang N.: Multi-objective generation scheduling of integrated energy
system using hybrid optimization technique, Neural Computing and Applications,
vol. 36(3), pp. 1215–1236, 2024.
[18] Kumar P., Kumar R.: Improved Active Monitoring Load-Balancing Algorithm
in Cloud Computing. In: Proceedings of 2nd International Conference on Com-
munication, Computing and Networking: ICCCN 2018, NITTTR Chandigarh,
India, pp. 1033–1040, Springer, 2019.
[19] Li G., Wu Z.: Ant colony optimization task scheduling algorithm for SWIM
based on load balancing, Future Internet, vol. 11(4), p. 90, 2019. doi: 10.3390/
fi11040090.
[20] Milan S.T., Rajabion L., Ranjbar H., Navimipour N.J.: Nature inspired
meta-heuristic algorithms for solving the load-balancing problem in cloud en-
vironments, Computers & Operations Research, vol. 110, pp. 159–187, 2019.
doi: 10.1016/j.cor.2019.05.022.
[21] Mohanty S., Patra P.K., Ray M., Mohapatra S.: An approach for load balancing
in cloud computing using JAYA algorithm, International Journal of Informa-
tion Technology and Web Engineering (IJITWE), vol. 14(1), pp. 27–41, 2019.
doi: 10.4018/ijitwe.2019010102.
[22] Narale S.A., Butey P.: Throttled load balancing scheduling policy assist to re-
duce grand total cost and data center processing time in cloud environment using
cloud analyst. In: 2018 Second International Conference on Inventive Communi-
cation and Computational Technologies (ICICCT), pp. 1464–1467, IEEE, 2018.
doi: 10.1109/icicct.2018.8473062.
[23] Negi S., Rauthan M.M.S., Vaisla K.S., Panwar N.: CMODLB: an efficient load
balancing approach in cloud computing environment, The Journal of Supercom-
puting, vol. 77(8), pp. 8787–8839, 2021. doi: 10.1007/s11227-020-03601-7.
[24] Prassanna J., Venkataraman N.: Adaptive regressive holt–winters workload pre-
diction and firefly optimized lottery scheduling for load balancing in cloud, Wire-
less Networks, vol. 27(8), pp. 5597–5615, 2021.
[25] Semmoud A., Hakem M., Benmammar B., Charr J.C.: Load balancing in
cloud computing environments based on adaptive starvation threshold, Concur-
rency and Computation: Practice and Experience, vol. 32(11), p. e5652, 2020.
doi: 10.1002/cpe.5652.
[26] Sethi N., Singh S., Singh G.: Improved mutation-based particle swarm opti-
mization for load balancing in cloud data centers. In: Harmony Search and Na-
ture Inspired Optimization Algorithms: Theory and Applications, ICHSA 2018,
pp. 939–947, Springer, 2019.
[27] Shafiq D.A., Jhanjhi N.Z., Abdullah A., Alzain M.A.: A load balancing algorithm
for the data centres to optimize cloud computing applications, IEEE Access,
vol. 9, pp. 41731–41744, 2021. doi: 10.1109/access.2021.3065308.
[28] Siddiqui S., Darbari M., Yagyasen D.: An QPSL queuing model for load balancing
in cloud computing, International Journal of e-Collaboration (IJeC), vol. 16(3),
pp. 33–48, 2020. doi: 10.4018/ijec.2020070103.
[29] Singh I., Dhillon S.K.: Optimization Techniques for Mechatronics: A Compre-
hensive Review and Future Directions, Computational Intelligent Techniques in
Mechatronics, pp. 109–133, 2024. doi: 10.1002/9781394175437.ch4.
[30] Thirumal K., Sakthivel V., Sathya P.: Solution for short-term generation schedul-
ing of cascaded hydrothermal system with turbulent water flow optimization,
Expert Systems with Applications, vol. 213, p. 118967, 2023. doi: 10.1016/
j.eswa.2022.118967.
[31] Tyagi N., Rana A., Kansal V.: Creating elasticity with enhanced weighted opti-
mization load balancing algorithm in cloud computing. In: 2019 Amity Interna-
tional Conference on Artificial Intelligence (AICAI), pp. 600–604, IEEE, 2019.
doi: 10.1109/aicai.2019.8701375.
[32] Xingjun L., Zhiwei S., Hongping C., Mohammed B.O.: A new fuzzy-based
method for load balancing in the cloud-based Internet of things using a grey
wolf optimization algorithm, International Journal of Communication Systems,
vol. 33(8), p. e4370, 2020. doi: 10.1002/dac.4370.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Computer Science
This work is licensed under a Creative Commons Attribution 4.0 International License.
