A New Load Balancing Algorithm with Fuzzy Logic Controller in Grid Computing

doi:10.23940/ijpe.22.12.p2.844853

Abstract

Abstract: Load balancing has an important role in grid computing for managing jobs and allocating them to adequate resources. An efficient load balancing algorithm can minimize execution time and maximize resource utilization by achieving the load balance between resources in the grid. In this paper, a simple and efficient fuzzy-based load-balancing algorithm is proposed that not only offers efficient resource management but also ensures high utilization of dynamic resources. In this paper, we detail the design and implementation of a proposed fuzzy-logic-based load balancing algorithm in grid computing. The proposed algorithm works by using a fuzzy logic inference system that uses some metrics to capture the load of CPU and the queue length for specifying the state of each node. Then, based on the overall nodes' states, the state of the corresponding cluster will be defined to allocate the newly arrived jobs such that load balancing among different clusters and nodes is performed. The performance of the proposed algorithm is evaluated based on resource utilization, response time, and tardiness, and it is compared with scheduling algorithms like First Come First Serve (FCFS), SJF(Shortest Job First), Earliest Deadline First (EDF), and Job Migration Algorithm for Dynamic Load Balancing (JMADLB). To analyze the performance of fuzzy based load balancing algorithm, a simulation is carried out using two Java library jFuzzyLogic and GridSim simulator. The results are presented in detail.

Key words: grid computing, fuzzy logic, load balancing, load of CPU, queue length

Ali Wided. A New Load Balancing Algorithm with Fuzzy Logic Controller in Grid Computing [J]. Int J Performability Eng, 2022, 18(12): 844-853.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References

1. Wided A., Okba K., andFatima B.Load Balancing with Job Migration Algorithm for Improving Performance on Grid Computing: Experimental Results. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, vol. 8, no. 4, pp.5-18, 2019.
2. Sethi S., Sahu A., andJena S.K.Efficient Load Balancing in Cloud Computing using Fuzzy Logic. IOSR Journal of Engineering, vol. 2, no. 7, pp.65-71, 2012.
3. Khalilian, Z. and Mirabedini, S.J.A Novel Decentralized Fuzzy Based Approach for Grid Job. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 6, no. 1, pp. 21-26, 2014.
4. Zavvar M., Rezaei M., Garavand S., andRamezani F.Fuzzy Logic-Based Algorithm Resource Scheduling for Improving the Reliability of Cloud Computing. Asia-Pacific Journal of Information Technology and Multimedia, vol. 5, no. 1, pp. 39-48, 2016.
5. Hajoui Y., Bouattane O., Youssfi M., andIlloussamen E.New Hybrid Task Scheduling Algorithm with Fuzzy Logic Controller in Grid Computing. International Journal of Advanced Computer Science and Applications, vol. 9, no. 8, 2018.
6. Khan, A.I., Kazmi, S.A.R., Atta, A., Mushtaq, M.F., Idrees, M., Fakir, I., Safyan, M., Khan, M.A., and Qasim, A. Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic, 2021.
7. Rathore N.K.Efficient Agent Based Priority Scheduling and LoadBalancing using Fuzzy Logic in Grid Computing. system, vol. 6, pp. 13-23, 2015.
8. Velde, V. and Rama, B.An Advanced Algorithm for Load Balancing in Cloud Computing using Fuzzy Technique. In2017 International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, pp. 1042-1047, 2017.
9. Cingolani, P. and Alcalá-Fdez, J. jFuzzyLogic: A Java Library to Design Fuzzy Logic Controllers According to the Standard for Fuzzy Control Programming. International Journal of Computational Intelligence Systems, vol. 6, no. sup1, pp. 61-75, 2013.
10. Buyya, R. and Murshed, M.Gridsim: A Toolkit for the Modeling and Simulation of Distributed Resource Management and Scheduling for Grid Computing. Concurrency and computation: practice and experience, vol.14, no. 13‐15, pp. 1175-1220, 2002.

[1]	Bhushan Nandwalkar, Sukruta Pardeshi, Makarand Shahade, and Ashish Awate. Descriptive Handwritten Paper Grading System using NLP and Fuzzy Logic [J]. Int J Performability Eng, 2023, 19(4): 273-282.
[2]	Sushant Jhingran, Mayank Kumar Goyal, and Nitin Rakesh. DQLC: A Novel Algorithm to Enhance Performance of Applications in Cloud Environment [J]. Int J Performability Eng, 2023, 19(12): 771-778.
[3]	Priti Kumari and Parmeet Kaur. An Adaptable Approach to Fault Tolerance in Cloud Computing [J]. Int J Performability Eng, 2023, 19(1): 43-54.
[4]	Parul Kalra,Deepti Mehtrotra,and Abdul Wahid. Mapping User Information with Cognitive Skills by Hybrid IR Models with Inference Engine [J]. Int J Performability Eng, 2020, 16(9): 1341-1350.
[5]	Ahmed Bounit, Elhassan Irhirane, Badr Dakkak, Abdelatif Rajji, and Abaida Abdellah. Evaluation Model of the Production System Effectiveness [J]. Int J Performability Eng, 2019, 15(8): 2031-2039.
[6]	Guoyan Li, Xinqiang Wang, Zhigang Zhang, Yadong Chen, and Shudong Liu. A Scalable Load Balancing Scheme for Software-Defined Datacenter Networks based on Fuzzy Logic [J]. Int J Performability Eng, 2019, 15(8): 2217-2227.