Dynamic Load Balancing in Cloud Computing: A Convergence of PSO, GSA, and Fuzzy Logic within a Hybridized Metaheuristic Framework

Full Text (PDF, 667KB), PP.44-55

Views: 0 Downloads: 0

Author(s)

Rajgopal K T 1 Abhishek S. Rao 2,* Ramaprasad Poojary 3 Deepak D 2

1. Department of Computer Science and Engineering, Canara Engineering College, Benjanapadavu, Karnataka, India

2. Department of Information Science and Engineering, Nitte (Deemed to be University), NMAM Institute of Technology, Nitte, Karnataka, India

3. School of Engineering & IT, Manipal Academy of Higher Education, Dubai Campus, Dubai, UAE

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2023.06.04

Received: 14 May 2023 / Revised: 24 Aug. 2023 / Accepted: 13 Sep. 2023 / Published: 8 Dec. 2023

Index Terms

Cloud Computing, Fuzzy Logic, Gravitational Search Algorithm, Load Balancing, NP-hard problem, Particle Swarm Optimization

Abstract

In the recent era, there has been a significant surge in the demand for cloud computing due to its versatile applications in real-time situations. Cloud computing efficiently tackles extensive computing challenges, providing a cost-effective and energy-efficient solution for cloud service providers (CSPs). However, the surge in task requests has led to an overload on cloud servers, resulting in performance degradation. To address this problem, load balancing has emerged as a favorable approach, wherein incoming tasks are allocated to the most appropriate virtual machine (VM) according to their specific needs. However, finding the optimal VM poses a challenge as it is considered a difficult problem known as NP-hard. To address this challenge, current research has widely adopted meta-heuristic approaches for solving NP-hard problems. This research introduces a novel hybrid optimization approach, integrating the particle swarm optimization algorithm (PSO) to handle optimization, the gravitational search algorithm (GSA) to improve the search process, and leveraging fuzzy logic to create an effective rule for selecting virtual machines (VMs) efficiently. The integration of PSO and GSA results in a streamlined process for updating particle velocity and position, while the utilization of fuzzy logic assists in discerning the optimal solution for individual tasks. We assess the efficacy of our suggested method by gauging its performance through various metrics, including throughput, makespan, and execution time. In terms of performance, the suggested method demonstrates commendable performance, with average load, turnaround time, and response time measuring at 0.168, 18.20 milliseconds, and 11.26 milliseconds, respectively. Furthermore, the proposed method achieves an average makespan of 92.5 milliseconds and average throughput performance of 85.75. The performance of the intended method is improved by 90.5%, 64.9%, 36.11%, 24.72%, 18.27%, 11.36%, and 5.21 in comparison to the existing techniques. The results demonstrate the efficacy of this approach through significant improvements in execution time, CPU utilization, makespan, and throughput, providing a valuable contribution to the field of cloud computing load balancing.

Cite This Paper

Rajgopal K T, Abhishek S. Rao, Ramaprasad Poojary, Deepak D, "Dynamic Load Balancing in Cloud Computing: A Convergence of PSO, GSA, and Fuzzy Logic within a Hybridized Metaheuristic Framework", International Journal of Modern Education and Computer Science(IJMECS), Vol.15, No.6, pp. 44-55, 2023. DOI:10.5815/ijmecs.2023.06.04

Reference

[1]Rodriguez, M. A., & Buyya, R. (2018). Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Future Generation Computer Systems, 79, 739-750.
[2]Afzal, S., & Kavitha, G. (2019). Load balancing in cloud computing–A hierarchical taxonomical classification. Journal of Cloud Computing, 8(1), 1-24.
[3]Havanje, N. S., Kumar, K. R. A., Shenoy, S. N., Rao, A. S., & Thimmappayya, R. K. (2022). Secure and Reliable Data Access Control Mechanism in Multi-Cloud Environment with Inter-Server Communication Security. Suranaree Journal of Science and Technology, 29(3).
[4]Olokunde, T., Misra, S., & Adewumi, A. (2017, October). Quality model for evaluating platform as a service in cloud computing. In International Conference on Information and Software Technologies (pp. 280-291). Springer, Cham.
[5]Hussein, M. K., Mousa, M. H., & Alqarni, M. A. (2019). A placement architecture for a container as a service (CaaS) in a cloud environment. Journal of Cloud Computing, 8(1), 1-15.
[6]Khan, F. A., Jamjoom, M., Ahmad, A., & Asif, M. (2022). An analytic study of architecture, security, privacy, query processing, and performance evaluation of database‐as‐a‐service. Transactions on emerging telecommunications technologies, 33(2), e3814.
[7]Navimipour, N. J., Rahmani, A. M., Navin, A. H., & Hosseinzadeh, M. (2015). Expert Cloud: A Cloud-based framework to share the knowledge and skills of human resources. Computers in Human Behavior, 46, 57-74.
[8]Gaikwad, C., Churi, B., Patil, K., & Tatwadarshi, P. N. (2017, March). Providing storage as a service on cloud using OpenStack. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1-4). IEEE.
[9]Zargar, S. T., Takabi, H., & Iyer, J. (2019). Security-as-a-Service (SECaaS) in the cloud. Security, Privacy, and Digital Forensics in the Cloud, Chap. 9.
[10]Tung, Y. H., Lin, C. C., & Shan, H. L. (2014, April). Test as a Service: A framework for Web security TaaS service in cloud environment. In 2014 IEEE 8th International Symposium on Service Oriented System Engineering (pp. 212-217). IEEE.
[11]Milani, A. S., & Navimipour, N. J. (2016). Load balancing mechanisms and techniques in the cloud environments: Systematic literature review and future trends. Journal of Network and Computer Applications, 71, 86-98.
[12]Tundo, A., Mobilio, M., Orrù, M., Riganelli, O., Guzmàn, M., & Mariani, L. (2019, August). Varys: An agnostic model-driven monitoring-as-a-service framework for the cloud. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 1085-1089).
[13]Ghomi, E. J., Rahmani, A. M., & Qader, N. N. (2017). Load-balancing algorithms in cloud computing: A survey. Journal of Network and Computer Applications, 88, 50-71.
[14]Milan, S. T., Rajabion, L., Ranjbar, H., & Navimipour, N. J. (2019). Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments. Computers & Operations Research, 110, 159-187.
[15]Gamal, M., Rizk, R., Mahdi, H., & Elnaghi, B. E. (2019). Osmotic bio-inspired load balancing algorithm in cloud computing. IEEE Access, 7, 42735-42744.
[16]Devaraj, A. F. S., Elhoseny, M., Dhanasekaran, S., Lydia, E. L., & Shankar, K. (2020). Hybridization of firefly and improved multi-objective particle swarm optimization algorithm for energy efficient load balancing in cloud computing environments. Journal of Parallel and Distributed Computing, 142, 36-45.
[17]Polepally, V., & Shahu Chatrapati, K. (2019). Dragonfly optimization and constraint measure-based load balancing in cloud computing. Cluster Computing, 22(1), 1099-1111.
[18]Kaur, A., & Kaur, B. (2019). Load balancing optimization based on hybrid Heuristic-Metaheuristic techniques in cloud environment. Journal of King Saud University-Computer and Information Sciences.
[19]Adhikari, M., Nandy, S., & Amgoth, T. (2019). Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud. Journal of Network and Computer Applications, 128, 64-77.
[20]Arul Xavier, V. M., & Annadurai, S. (2019). Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Cluster Computing, 22(1), 287-297.
[21]Mansouri, N., Zade, B. M. H., & Javidi, M. M. (2019). Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory. Computers & Industrial Engineering, 130, 597-633.
[22]Torabi, S., & Safi-Esfahani, F. (2018). A dynamic task scheduling framework based on chicken swarm and improved raven roosting optimization methods in cloud computing. The Journal of Supercomputing, 74(6), 2581-2626.
[23]Ye, X., Yin, Y., & Lan, L. (2017). Energy-efficient many-objective virtual machine placement optimization in a cloud computing environment. IEEE access, 5, 16006-16020.
[24]Li, J., & Dong, N. (2017, December). Gravitational search algorithm with a new technique. In 2017 13th International Conference on Computational Intelligence and Security (CIS) (pp. 516-519). IEEE.