Ford Fulkerson and Newey West Regression Based Dynamic Load Balancing in Cloud Computing for Data Communication

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Prabhakara B. K. 1,* Chandrakant Naikodi 2 Suresh L. 3

1. Department of Information Science and Engineering, A J Institute of Engineering and Technology, Mangaluru, Affiliated to VTU Belagavi, India

2. Department of Studies and Research in Computer Science (PG), Davangere University, Davangere, India

3. Department of Information Science and Engineering, RNS Institute of Technology, Bengaluru, Affiliated to VTU Belagavi, India

* Corresponding author.


Received: 27 Dec. 2022 / Revised: 27 Feb. 2023 / Accepted: 6 May 2023 / Published: 8 Oct. 2023

Index Terms

Cloud Computing, Task Scheduling, Ford Fulkerson, Load Balancing, Machine Learning, Newey West Regression


In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.

Cite This Paper

Prabhakara B. K., Chandrakant Naikodi, Suresh L., "Ford Fulkerson and Newey West Regression Based Dynamic Load Balancing in Cloud Computing for Data Communication", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.5, pp.81-95, 2023. DOI:10.5815/ijcnis.2023.05.08


[1]Stavros Souravlas, Sofia D. Anastasiadou, Nicoleta Tantalak, Stefanos Katsavounis, “A Fair, Dynamic Load Balanced Task Distribution Strategy for Heterogeneous Cloud Platforms Based on Markov Process Modeling”, IEEE Access, Mar 2022
[2]Mayank Sohani, S. C. Jain, “A Predictive Priority-Based Dynamic Resource Provisioning Scheme with Load Balancing in Heterogeneous Cloud Computing”, IEEE Access, Apr 2021
[3]Xiaoxun Zhong, Lianming Zhang, Yehua Wei, “Dynamic Load-Balancing Vertical Control for a Large-Scale Software-Defined Internet of Things”, IEEE Things, Jul 2019
[4]Daniel Casini, Alessandro Biondi, Giorgio Buttazzo, “Task Splitting and Load Balancing of Dynamic Real-Time Workloads for Semi-Partitioned EDF”, IEEE Transactions on Computers, Oct 2020
[5]Muhammad Junaid, Adnan Sohail, Rao Naveed Bin Rais, Adeel Ahmed, Osman Khalid, Imran Ali Khan, Syed Sajid Hussain, Naveed Ejaz, “Modeling an Optimized Approach for Load Balancing in Cloud”, IEEE Access, Oct 2020
[6]Mirza Mohd Shahriar Maswood, Md. Rahinur Rahman, Abdullah G. Alharbi, Deep Medhi, “A Novel Strategy to Achieve Bandwidth Cost Reduction and Load Balancing in a Cooperative Three-Layer Fog-Cloud Computing Environment”, IEEE Access, Jun 2020
[7]Zhiyu Liu, Aqun Zhao and Mangui Liang, “A port-based forwarding load-balancing scheduling approach for cloud datacenter networks”, Journal of Cloud Computing: Advances, Systems and Applications, Nov 2021
[8]Eswaran Sivaraman and R. Manickachezian, “Unevenness measurement using the support vector machine and dynamic multiservice load balancing with modified genetic algorithm in cloud-based multimedia system”, International Journal of Computer Aided Engineering and Technology, Vol. 10, No. 6, Nov 2018
[9]Guangshun Li, Yonghui Yao, Junhua Wu, Xiaoxiao Liu, Xiaofei Sheng and Qingyan Lin, “A new load balancing strategy by task allocation in edge computing based on intermediary nodes”, EURASIP Journal onWireless Communications and Networking, Springer, Jul 2020
[10]Dalia Abdulkareem Shafiq, N.Z. Jhanjhi, Azween Abdullah, “Load balancing techniques in cloud computing environment: A review”, Journal of King Saud University – Computer and Information Sciences, Elsevier, Mar 2021
[11]Shahbaz Afzal and G. Kavitha, “Load balancing in cloud computing – A hierarchical taxonomical classification”, Journal of Cloud Computing: Advances, Systems and Applications, Jul 2019
[12]Emre Gures, Ibraheem Shayea, Mustafa Ergen, Marwan Hadri Azmi and Ayman A. EL-SALEH, “Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey”, IEEE Access, Apr 2022
[13]Vahid Mohammadian, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Aso Darwesh, “Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review”, IEEE Access, Dec 2021
[14]Tawfeed Mohmmed Tawfeeg, Adil Yousif, Alzubair Hassan, Samar M. Alqhtani, Rafik Hamza, Mohammed Bakri Bashir, Awad Ali, “Cloud Dynamic Load Balancing and Reactive Fault Tolerance Techniques: A Systematic Literature Review (SLR)”, IEEE Access, Jul 2022
[15]Muhammad Asim Shahid, Noman Islam, Muhammad Mansoor Alam, Mazliham Mohd Suud, Shahrulniza Musa, “A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach”, IEEE Access, Jul 2020
[16]K.P.N Jayasena, K.M.S.U Bandaranayake, y, B. T. G. S. Kumara, “TRETA - A Novel Heuristic Based Efficient Task Scheduling Algorithm in Cloud Environment”, IEEE Xplore, Jun 2021
[17]Sambit Kumar Mishra, Bibhudatta Sahoo, Priti Paramita Parida, “Load balancing in cloud computing: A big picture”, Journal of King Saud University – Computer and Information Sciences, Elsevier, Feb 2018
[18]C´eline Comtea, “Dynamic Load Balancing with Tokens”, Computer Communications, Elsevier, Sep 2019
[19]Dong-sheng Liu and Xiao-hong Xiao, “Load balancing algorithm based on multiple linear regression analysis in multi-agent systems”, International Journal of Computational Science and Engineering, Vol. 16, No. 3, 2018
[20]Neha Gupta, Kamali Gupta, Deepali Gupta, Sapna Juneja, Hamza Turabieh, Gaurav Dhiman, Sandeep Kautish, and Wattana Viriyasitavat, “Enhanced Virtualization-Based Dynamic Bin-Packing Optimized Energy Management Solution for Heterogeneous Clouds”, Mathematical Problems in Engineering, Hindawi, Jan 2022