COTSA: A Load-Balanced Task Scheduling Algorithm using Coati Optimization in Cloud Computing Environment

Document Type : Research Article

Authors

Department of Computer Science, Shahid Bahonar University of Kerman, Kerman, Iran.

Abstract

During the scheduling process, it is important to respect the constraints given by the jobs and the cloud providers. In addition to maintaining a balance between Quality of Service (QoS), fairness, and efficiency of jobs, scheduling is challenging. This paper aims to propose an efficient algorithm for load-balanced task scheduling in the cloud. Our algorithm uses a new meta-heuristic algorithm called COA (Coati Optimization Algorithm) to solve the task scheduling problem. This method is called COTSA (Coati Optimization-based Task Scheduling Algorithm). Its main goal is to reduce execution costs, load balancing, resource consumption, and makepan. Additionally, experimental results indicate that COTSA contributes to reduced energy consumption and enhanced system scalability and fault tolerance under simulated conditions. These improvements suggest potential suitability for dynamic and large-scale cloud infrastructures, though performance may vary depending on workload characteristics and system configurations. It is compared with Walrus Optimizer (WO), Slap Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Zebra Optimization Algorithms (ZOA), Grasshopper Optimization Algorithm (GOA), Sooty Tern Optimization Algorithm (STOA), Golden Eagle Optimizer (GEO), Grey Wolf Optimizer (GWO), Subtraction-Average-Based Optimizer (SABO), and Sand Cat Swarm Optimization (SCSO), which are popular meta-heuristics. Experimental results demonstrate that COTSA reduces makespan by approximately 9%, lowers execution cost by up to 40%, improves resource utilization by around 3%, and enhances load balance by up to 30%, energy consumption about 36%, scalability near 17%, and fault tolerance about 16%, making it a robust and scalable solution for efficient cloud task scheduling.

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References

[1] Laroui M, Nour B, Moungla H, Cherif MA, Afifi H, Guizani M. Edge and fog computing for IoT: A survey on current research activities & future directions. Computer Communications. 2021; 180:210-231.
[2] ITU Telecommunication Development Bureau, ICT facts and figures, 2017.
[3] Bellavista P, Berrocal J, Corradi A, Das SK, Foschini L, Zanni A. A survey on fog computing for the Internet of Things. Pervasive and Mobile Computing. 2019; 52:71-99.
[4] Mansouri N, Mohammad Hasani Zade B, Javidi MM. Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Computers & Industrial Engineering. 2019; 130:597-633.
[5] Pradhan A, Bisoy SK, Das A. A survey on PSO-based meta-heuristic scheduling mechanism in cloud computing environment. Journal of King Saud University – Computer and Information Sciences. 2022; 34:4888-4901.
[6] Alworafi, MA, Mallappa, S. A collaboration of deadline and budget constraints for task scheduling in cloud computing. Cluster Computing. 2019;1-11.
[7] Sangaiah AK, Hosseinabadi AR, Shareh MB, Bozorgi Rad SY, Zolfagharian A, Chilamkurti N. IoT resource allocation and optimization based on heuristic algorithm. Sensors. 2020; 20(2):1-26.
[8] Topcuoglu H, Hariri S, Wu M-Y. Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distributed Systems. 2002; 13(3):260-274.
[9] Pirozmand P, Rahmani Hosseinabadi AA, Farrokhzad M, Sadeghilalimi M, Mirkamali S, Slowik A. Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing. Neural Computing and Applications. 2021; .
[10] Madni SHH, Abd Latiff MS, Ali J. Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment. Cluster Computing. 2019; 22:301-334.
[11] Dehghani M, Montazeri Z, Dehghani A, Malik OP, Morales-Menendez R, Dhiman G, Nouri N, Ehsanifar A, Guerrero JM, Ramirez-Mendoza RA. Binary spring search algorithm for solving various optimization problems. Applied Sciences. 2021; 11(3):1286.
[12] Dehghani M, Montazeri Z, Trojovská E, Trojovský P. Coati Optimization Algorithm: A new bio-inspired metaheuristic algorithm for solving optimization problems. Knowledge-Based Systems. 2023; 259:110011.
[13] Cuarón A, Helgen K, Reid F, Pino J, González-Maya J, narica N. The IUCN red list of threatened species 2016: e. T41683A45216060.
[14] Manikandan N, Divya P, Janani S. BWFSO: Hybrid Black-widow and Fish swarm optimization Algorithm for resource allocation and task scheduling in cloud computing. Materials Today: Proceedings. 2022; 62:4903-4908.
[15] Hassan M, Al-Awady AA, Ali A, Munawar Iqbal M, Akram M, Khan J, Abu-Odeh AA. An efficient dynamic decision-based task optimization and scheduling approach for microservice-based cost management in mobile cloud computing applications. Pervasive and Mobile Computing 2023; 92:101785.
[16] Sanaj MS, Prathap PMJ. Nature-inspired chaotic squirrel search algorithm (CSSA) for multi-objective task scheduling in an IAAS cloud computing atmosphere. Engineering Science and Technology, an International Journal. 2020; 23:891-902.
[17] Velliangiri S, Karthikeyan P, Xavier VMA, Baswaraj D. Hybrid electro search with genetic algorithm for task scheduling in cloud computing. Ain Shams Engineering Journal. 2021; 12:631-639.
[18] Mangalampalli S, Karri GR, Kose U. Multi-objective trust-aware task scheduling algorithm in cloud computing using whale optimization. Journal of King Saud University – Computer and Information Sciences. 2023; 35:791-809.
[19] Han M, Du Z, Yuen KF, Zhu H, Li Y, Yuan Q. Walrus optimizer: A novel nature-inspired metaheuristic algorithm. Expert Systems with Applications. 2024; 239:122413.
[20] Mirjalili SA, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software. 2017; 114:163-191.
[21] Mirjalili SA, Lewis A. The whale optimization algorithm. Advances in Engineering Software. 2016; 95:51-67.
[22] Trojovska E, Dehghani M, Trojovsky P. Zebra optimization algorithm: A new bio-inspired optimization algorithm for Solving Optimization Algorithm. IEEE Access. 2022; 10:49445-49473.
[23] Saremi S, Mirjalili S, Lewis A. Grasshopper optimization algorithm: Theory and application. Advances in engineering software, 2017; 105:30-47.
[24] Dhiman G, Kaur A, STOA: A bio-inspired based optimization algorithm for industrial engineering problems. Engineering Applications of Artificial Intelligence, 2019; 82:148-174.
[25] Mohammadi-Balani A, Nayeri MD, Azar A, Taghizadeh-Yazdi M. Golden eagle optimizer: A nature-inspired metaheuristic algorithm. Computers & Industrial Engineering, 2021; 152:107050.
[26] Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer.Advances in Engineering Software, 2014; 69:46-61.
[27] Trojovskỳ P, Dehghani M. Subtraction-average-based optimizer: A new swarm-inspired metaheuristic algorithm for solving optimization problems. Biomimetics, 2023; 8(2):149.
[28] Seyyedabbasi A, Kiani F. Sand cat swarm optimization: A nature-inspired algorithm to solve global optimization problems. Engineering with Computers, 2023; 39(4):2627- 2651.
AUT Journal of Modeling and Simulation
Volume 57, Issue 1
June 2025
Pages 89-112

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