HKCHB: Meta-heuristic Algorithm for Task Scheduling and Load Balancing in Cloud-fog Computing

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Mahmoud Moshref 1,* Sherin Hijazi 1 Azzam Sleit 2 Ahmad Sharieh 2

1. Palestine Technical University -Kadoorie/ Department of Computer Science/ Faculty of Information Technology, Tulkarm, P.O Box 305, Palestine

2. The University of Jordan/ Department of Computer Science/ King Abdullah II School for Information Technology, Amman, 11941, Jordan

* Corresponding author.


Received: 16 Jan. 2024 / Revised: 15 Feb. 2024 / Accepted: 17 Mar. 2024 / Published: 8 Jun. 2024

Index Terms

Cloud computing, Fog computing, Internet of Things, Task scheduling, Load Balance, Honey Bee, Clustering, k-Means


Cloud-fog computing has emerged as the contemporary approach for processing and analyzing Internet of Things applications due to its ability to offer remote resources. Cloud fog computing technology provides shared resources, information, and software packages, supporting distributed parallel systems in an open environment. It constructs and manages virtual machines to enhance efficiency and attractiveness. We have consistently strived to tackle challenges affecting the efficiency of cloud fog computing, including ineffective resource utilization and response times. The improvement of these challenges can be achieved through effective task scheduling and load balancing between Virtual Machines, this problem considered as NP-hard problem. This paper proposes a Hybrid K-means Clustering Honey Bee algorithm (HKCHB) to cluster Virtual Machines into two or more clusters. Subsequently, the hybrid Honey Bee algorithm is employed for task scheduling, enhancing load balance performance. The proposed algorithm is compared with other task scheduling and load balancing algorithms, including Round Robin, Ant Colony, Honey Bee, and Particle Swarm Optimization Algorithm, utilizing the CloudSim Simulator. The results demonstrate the superiority of the proposed algorithm, yielding the lowest response time. Specifically, the response time is reduced by 22.1%, and processing time is reduced by 47.9%, while throughput is increased by 95.4%. These improvements are observed under the assumption of multiple tasks in a heterogeneous environment, utilizing one or two Data Centers with Virtual Machines. This contribution gives the impression that network systems based on the Internet of Things and cloud fog computing will be improved in the future to operate within the framework of real-time systems with high efficiency.

Cite This Paper

Mahmoud Moshref, Sherin Hijazi, Azzam Sleit, Ahmad Sharieh, "HKCHB: Meta-heuristic Algorithm for Task Scheduling and Load Balancing in Cloud-fog Computing", International Journal of Engineering and Manufacturing (IJEM), Vol.14, No.3, pp. 1-15, 2024. DOI:10.5815/ijem.2024.03.01


[1]Tran Cong Hung, and Nguyen Xuan Phi, “STUDY THE EFFECT OF PARAMETERS TO LOAD BALANCING IN CLOUD COMPUTING,” International Journal of Computer Networks & Communications (IJCNC), Vol.8, No.3, May 2016.
[2]Kunal S, Saha A, Amin R, “An overview of cloud-fog computing: Architectures, applications with security challenges,” Security and Privacy, 2019.
[3]Sabireen H., Neelanarayanan V., “A Review on Fog Computing: Architecture, Fog with IoT,” Algorithms and Research Challenges, ICT Express, Volume 7, Issue 2, 2021,
[4]Mohamed Abd Elaziz, Laith Abualigah, Ibrahim Attiya, “Advanced optimization technique for scheduling IoT tasks in cloud-fog computing environments,” Future Generation Computer Systems, Volume 124, 2021,
[5]M. Santhosh Kumar, K. Ganesh Reddy, Rakesh Kumar Donthi, “SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing,” International Journal of Information Technology and Computer Science, Vol.16, No.1, pp.1-12, 2024.
[6]Gurpreet Singh Bedi, “An Efficient Load Balancing based on Resource Utilization in Cloud Computing,” Master of Engineering in Software Engineering, 147004, June 2014.
[7]Hafiz Jabr Younis, Alaa Al Halees, and Mohammed Radi, “Hybrid Load Balancing Algorithm in Heterogeneous Cloud Environment,” International Journal of Soft Computing and Engineering (IJSCE), Volume-5 Issue-3, July 2015.
[8]Jafar Shayan, Ahmad Azarnik, Suriayati Chuprat, Sasan Karamizadeh, and Mojtaba Alizadeh, “Identifying Benefits and Risks Associated with Utilizing Cloud Computing,” The International Journal of Soft Computing and Software Engineering [JSCSE], Vol. 3, No. 3, 2013.
[9]Ali Al-maamari, and Fatma A. Omara, “Task Scheduling Using PSO Algorithm in Cloud Computing Environments,” International Journal of Grid Distribution Computing Vol. 8, No.5, (2015).
[10]Mala Kalra, and Sarbject Singh, “A review of metaheuristic scheduling techniques in cloud computing,” Egyptian Information Journal, August (2015).  
[11]Ashima, and Mrs Navjot Jyoti, “Enhancing Job Allocation Using NBST in Cloud Environment: A Review,” International journal of computer and technology, June 2017.
[12]Kousik Dasgupta, Brototi Mandal, Paramartha Dutta, and Jyotsna Kumar Mondal, Santanu Dam, “A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing,” Elsevier, International Conference on Computational Intelligence: Modelling Techniques and Applications (CIMTA) 2013.
[13]Safwat A. Hamad, and Fatma A. Omara, “Genetic-Based Task Scheduling Algorithm in Cloud Computing Environment,” International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 7, No. 4, 2016.
[14]Gao, R.; Wu, J. “Dynamic Load Balancing Strategy for Cloud Computing with Ant Colony Optimization,” Future Internet 2015, 7, 465-483.
[15]A.I.Awad, N.A.El-Hefnawy, and H.M.Abdel_kader, “Enhanced Particle Swarm Optimization For Task Scheduling In Cloud Computing Environments,” Elsevier, International Conference on Communication, Management and Information Technology (ICCMIT),  2015. 
[16]Baris Yuce, Michael S. Packianather, Ernesto Mastrocinque, Duc Truong Pham, and Alfredo Lambiase, “Honey Bees Inspired Optimization Method: The Bees Algorithm,” mdpi Journal, Insects November 2013.
[17]Duc Truong, and Marco Castellani, “A comparative study of the Bee Algorithm as tool for function optimization,” Pham & Castellani, Cogent Engineering, 2015. 
[18]Bashar Al-Shboul, and Sung-Hyon Myaeng, “Initializing K-Means using Genetic Algorithms,” Conference Paper, November 2009.
[19]F. A. Saif, R. Latip, Z. M. Hanapi and K. Shafinah, “Multi-Objective Grey Wolf Optimizer Algorithm for Task Scheduling in Cloud-Fog Computing,” in IEEE Access, vol. 11, pp. 20635-20646, 2023m doi: 10.1109/ACCESS.2023.3241240.
[20]Hemant Kumar Apat, Bibhudutta Sahoo, Veena Goswami, Rabindra K. Barik, “A hybrid meta-heuristic algorithm for multi-objective IoT service placement in fog computing environments,” Decision Analytics Journal, Volume 10, 2024,
[21]M. Santhosh Kumar & Ganesh Reddy Karri, “AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm,” International Journal of Experimental Research and Review. 33, 41-56, 2023, DOI: ijerr. 2023.v33spl.005.
[22]Adam A. Alli, Muhammad Mahbub Alam, “The fog cloud of things: A survey on concepts, architecture, standards, tools, and applications,” Internet of Things, Volume 9, 2020,
[23]Jamil B, Shojafar M, Ahmed I, Ullah A, Munir K, Ijaz H, “A job scheduling algorithm for delay and performance optimization in fog computing Concurrency,” Computat Pract Exper, 2019; e5581.
[24]Einollah Jafarnejad Ghomi, Amir Masoud Rahmani, and Nooruldeen Nasih Qader, “Load-balancing algorithms in cloud computing: A survey,” ELSEVIER, Journal of Network and Computer Applications, Volume 88, 15 June 2017, Pages 50-71.
[25]Deepika, Divya Wadhwa, and Nitin Kumar, “Performance Analysis of Load Balancing Algorithms in Distributed System,” Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 4, Number 1, 2014. 
[26]Reena Panwar, Bhawna Mallick, “A Comparative Study of Load Balancing Algorithms in Cloud Computing,” International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 24, May 2015. 
[27]Sandip Patel, Ritesh Patel, Hetal Patel, and Seema Vahora, “CloudAnalyst: A Survey of Load Balancing Policies,” International Journal of Computer Applications (0975 – 8887) Volume 117 – No. 21, May 2015. 
[28]Abhijit Patil, Harshal Gala, and Jai Kapoor, “Dynamic Load Balancing in Cloud Computing using Swarm Intelligence Algorithms,” International Journal of Computer Applications (0975 – 8887) Volume 130 – No.15, November 2015.  
[29]D.T. Pham, A. Ghanbarzadeh, E. Koc, S. Otri, S. Rahim, and M. Zaidi, “Bee Algorithm A Novel Approach to Function Optimization,” Technical Note: MEC 0501, December 2005.
[30]Moore, Jacqueline and Richard Chapman, “Application of Particle Swarm to Multiobjective Optimization.” Computer Systems: Science & Engineering, 1999.
[31]C. A. Coello Coello and M. S. Lechuga, “MOPSO: a proposal for multiple objective particle swarm optimization,” Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), Honolulu, HI, USA, 2002, pp. 1051-1056 vol.2, doi: 10.1109/CEC.2002.1004388.
[32]Mahmoud Moshref, Rizik Al-Sayyed, Saleh Al-Sharaeh, “MULTI-OBJECTIVE OPTIMIZATION ALGORITHMS FOR WIRELESS SENSOR NETWORKS: A COMPREHENSIVE SURVEY,” Journal of Theoretical and Applied Information Technology, Vol.98. No 14, July 2020.
[33]Khadijah Bahwaireth, Loai Tawalbeh, Elhadj Benkhlifa, and Yaser Jararweh, “Experimental Comparison of Simulation Tools for Efficient Cloud and Mobile Cloud Computing Application,” EURASIP Journal on Information Security, June 2016.
[35]Mohamed Abdel-Basset, Doaa El-shahat, Mohamed Elhoseny, Houbing Song, “Energy-Aware Metaheuristic Algorithm for Industrial Internet of Things Task Scheduling Problems in Fog Computing Applications,” IEEE Internet of Things Journal, August 2020.