Systematic Review: Particle Swarm Optimization (PSO) based Load Balancing for Cloud Computing

Darakhshan Syed; Ghulam Muhammad Shaikh; Safdar Rizvi

doi:10.33317/ssurj.608

Authors

Darakhshan Syed Bahria University, Karachi Campus
Ghulam Muhammad Shaikh Bahria University Karachi Campus
Safdar Rizvi

DOI:

https://doi.org/10.33317/ssurj.608

Keywords:

Cloud Computing, Load Balancing, Metaheuristics, Particle Swarm Optimization, Review

Abstract

In this modern era cloud computing and widespread use of it have led to a massive spike in users' requests. The infrastructure's resources would either not be able to handle the increased volume of traffic or the scenario would lead to some resources being over or underutilized. Due to the unpredictable spike in traffic the system’s performance eventually grades. An optimal load distribution is therefore the area of concern for controlling traffic so that all available resources are fully used if possible in order to increase system efficiency. Static, heuristics-based, traditional dynamic and metaheuristic-based algorithms are ways that have been used for effective load balancing. The issue is challenging since there isn't a single best fit approach. The objective of this systematic review is to study the utilization of Particle Swarm Optimization method along with its proposed variations to distribute the incoming traffic evenly with efficient resource utilization.

References

Sajid, M., & Raza, Z. (2013, December). Cloud Computing: Issues & Challenges. In International Conference on Cloud, Big Data and Trust (Vol. 20, No. 13, pp. 13-15). sn.

Zhao, J., Yang, K., Wei, X., Ding, Y., Hu, L., & Xu, G. (2016). A Heuristic Clustering-Based Task Deployment Approach for Load Balancing Using Bayes Theorem in Cloud Environment. IEEE Transactions on Parallel and Distributed Systems: A Publication of the IEEE Computer Society, 27(2), 305–316.

Syed, D., Islam, N., Shabbir, M. H., & Manzar, S. B. (2022). Applications of Big Data in Smart Health Systems. Handbook of Research on Mathematical Modeling for Smart Healthcare Systems, 52-85.

Haris, M., & Khan, R. Z. (2022). A systematic Review on Load Balancing Tools and Techniques in Cloud Computing. Inventive Systems and Control: Proceedings of ICISC 2022, 503-521.

Celesti, A., Fazio, M., Villari, M., & Puliafito, A. (2012). Virtual machine provisioning through satellite communications in federated Cloud environments. Future Generation Computer

Systems, 28(1), 85-93.

Syed, D., Masood, U. B., Farwa, U. E., & Khurram, M. (2015). Cloud based Smart Irrigation for Agricultural Area of Pakistan. Computer Engineering and Applications Journal, 4(3), 153-164.

Khan, M. Z. A., Syed, D., & Karim, S. (2022). IoT Based COVID Patient Health Care and Monitoring System. ILMA Journal of Technology & Software Management, 3(1).

Buyya, R., Broberg, J., & Goscinski, A. M. (Eds.). (2010). Cloud Computing: Principles and Paradigms. John Wiley & Sons.

Mirtaheri, S. L., Fatemi, S. A., & Grandinetti, L. (2017). Adaptive Load Balancing Dashboard in Dynamic Distributed Systems. Supercomputing Frontiers and Innovations, 4(4), 34-49.

Elmagzoub, M. A., Syed, D., Shaikh, A., Islam, N., Alghamdi, A., & Rizwan, S. (2021). A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment. Electronics, 10(21), 2718.

Al Nuaimi, K., Mohamed, N., Al Nuaimi, M., & Al-Jaroodi, J. (2012, December). A Survey of Load Balancing in Cloud Computing: Challenges and Algorithms. In 2012 Second Symposium on Network Cloud Computing and Applications (pp. 137-142). IEEE.

Thakur, A., & Goraya, M. S. (2017). A Taxonomic Survey on Load Balancing in Cloud. Journal of Network and Computer Applications, 98, 43-57.

Shoja, H., Nahid, H., & Azizi, R. (2014, July). A Comparative Survey on Load Balancing Algorithms in Cloud Computing. In Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.

Gupta, H., & Sahu, K. (2014). Honey Bee Behavior Based Load Balancing of Tasks in Cloud Computing. International journal of Science and Research, 3(6), 842-846.

Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load Balancing in Cloud Computing: a Big Picture. Journal of King Saud UniversityComputer and Information Sciences, 32(2), 149-158.

Mishra, S. K., Puthal, D., Sahoo, B., Jena, S. K., & Obaidat, M. S. (2018). An Adaptive Task Allocation Technique for Green Cloud Computing. The Journal of Supercomputing, 74, 370-385.

Ibrahim, A. H., Faheem, H. E. D. M., Mahdy, Y. B., & Hedar, A. R. (2016). Resource Allocation Algorithm for GPUs in a Private Cloud. International Journal of Cloud Computing, 5(1-2), 45-56.

Jebalia, M., Ben Letaïfa, A., Hamdi, M., & Tabbane, S. (2015). An Overview on Coalitional Game-Theoretic Approaches for Resource Allocation in Cloud Computing Architectures. International Journal of Cloud Computing 22, 4(1), 63-77.

Noshy, M., Ibrahim, A., & Ali, H. A. (2018). Optimization of Live Virtual Machine Migration in Cloud Computing: A Survey and Future Directions. Journal of Network and Computer Applications, 110, 1-10.

Gkatzikis, L., & Koutsopoulos, I. (2013). Migrate or not? Exploiting Dynamic Task Migration in Mobile Cloud Computing Systems. IEEE Wireless Communications, 20(3), 24-32.

Jamshidi, P., Ahmad, A., & Pahl, C. (2013). Cloud Migration Research: a Systematic Review. IEEE Transactions on Cloud Computing, 1(2), 142-157.

Shamsinezhad, E., Shahbahrami, A., Hedayati, A., Zadeh, A. K., & Banirostam, H. (2013). Presentation Methods for Task Migration in Cloud Computing By Combination of Yu Router and Post-Copy. International Journal of Computer Science Issues (IJCSI), 10(4), 98.

Kumar, D., Gandhamal, A., Talbar, S., & Hani, A. F. M. (2018). Knee Articular Cartilage Segmentation from MR Images: A Review. ACM Computing Surveys (CSUR), 51(5), 1-29.

Mahafzah, B. A., Alshraideh, M., Abu-Kabeer, T. M., Ahmad, E. F., & Hamad, N. A. (2012). The Optical Chained-Cubic Tree Interconnection Network: Topological Structure and Properties.

Computers & Electrical Engineering, 38(2), 330-345.

Junaid, M., Sohail, A., Rais, R. N. B., Ahmed, A., Khalid, O., Khan, I. A., ... & Ejaz, N. (2020). Modeling An Optimized Approach for Load Balancing in Cloud. IEEE Access, 8, 173208-173226.

Afzal, S., & Kavitha, G. (2019). A Taxonomic Classification of Load Balancing Metrics: A Systematic Review. Proc. 33rd Indian Eng. Congr, 7.

Dokeroglu, T., Sevinc, E., Kucukyilmaz, T., & Cosar, A. (2019). A Survey on New Generation Metaheuristic Algorithms. Computers & Industrial Engineering, 137, 106040.

Yang, X. S. (2010). Nature-Inspired Metaheuristic Algorithms. Luniver press.

Abdel-Basset, M., Abdel-Fatah, L., & Sangaiah, A. K. (2018). Metaheuristic algorithms: A Comprehensive Review. Computational Intelligence for Multimedia Big Data on the Cloud With Engineering Applications, 185-231.

Gandomi, A. H., Yang, X. S., Talatahari, S., & Alavi, A. H. (2013). Metaheuristic Algorithms in Modeling and Optimization. Metaheuristic Applications in Structures and Infrastructures, 1, 1-24.

Liu, C., Li, K., & Li, K. (2018). A Game Approach to Multi-Servers Load Balancing with Load-Dependent Server Availability Consideration. IEEE Transactions on Cloud Computing, 9(1), 1-13.

Morales-Castañeda, B., Zaldivar, D., Cuevas, E., Fausto, F., & Rodríguez, A. (2020). A Better Balance in Metaheuristic Algorithms: Does it Exist?. Swarm and Evolutionary Computation, 54, 100671.

Kumar, A., & Devi, R. (2023, April). VM Migration and Resource Management using Meta Heuristic Technique in Cloud Computing Services. In 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)(pp. 1-6). IEEE.

Dorigo, M., & Stützle, T. (2003). The Ant Colony Optimization Metaheuristic: Algorithms, Applications, and Advances. Handbook of Metaheuristics, 250-285.

Rahman, M., Hassan, R., Ranjan, R., & Buyya, R. (2013). Adaptive Workflow Scheduling for Dynamic Grid and Cloud Computing Environment. Concurrency and Computation: Practice and

Experience, 25(13), 1816-1842.

Ilavarasan, E., & Thambidurai, P. (2007). Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments. Journal of Computer

Sciences, 3(2), 94-103.

Arabnejad, H., & Barbosa, J. G. (2013). List Scheduling Algorithm for Heterogeneous Systems by an Optimistic Cost Table. IEEE Transactions on Parallel and Distributed Systems, 25(3), 682-694.

Syed, D., Shaikh, G. M., Alshahrani, H., Hamdi, M., Alsulami, M., Shaikh, A., & Rizwan, S. (2024). A Comparative Analysis of Metaheuristic Techniques for High Availability Systems (September 2023). IEEE Access.

Kaur, A., & Kaur, B. (2022). Load Balancing Optimization Based on Hybrid Heuristic-Metaheuristic Techniques in Cloud Environment. Journal of King Saud University-Computer and

Information Sciences, 34(3), 813-824.

Padhy, R., & Pani, S. K. (2024, February). Optimizing Task Load Distribution in Cloud Environments via Dynamic Genetic Algorithm. In 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC) (pp. 302-307). IEEE.

Amer, D. A., Attiya, G., & Ziedan, I. (2024). An Efficient MultiObjective Scheduling Algorithm Based on Spider Monkey and Ant Colony Optimization in Cloud Computing. Cluster Computing,

(2), 1799-1819.

Kaliappan, S., Paranthaman, V., Kamal, M. R., Sudhakar, A. V. V., & Muthukannan, M. (2024, January). A Novel Approach of Particle Swarm and ANT Colony Optimization for Task Scheduling in Cloud. In 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 272-278). IEEE.

Ala’Anzy, M., & Othman, M. (2019). Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study. IEEE Access, 7, 141868-141887.

Ramezani, F., Lu, J., & Hussain, F. K. (2014). Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization. International Journal of Parallel Programming, 42, 739-754.

Alguliyev, R. M., Imamverdiyev, Y. N., & Abdullayeva, F. J.(2019). PSO-Based Load Balancing Method in Cloud Computing. Automatic Control and Computer Sciences, 53, 45-55.

Bala, K., & Kumar, A. (2017). A Hybrid Approach for Load Balancing: Using Random Forest and PSO Approach (RFPSO). International Journal of Advanced Research in Computer Science,

(5).

Sun, L., Song, X., & Chen, T. (2019). An Improved Convergence Particle Swarm Optimization Algorithm With Random Sampling of Control Parameters. Journal of Control Science and Engineering, 2019, 1-11.

Golchi, M. M., Saraeian, S., & Heydari, M. (2019). A Hybrid of Firefly and Improved Particle Swarm Optimization Algorithms for Load Balancing in Cloud Environments: Performance Evaluation. Computer Networks, 162, 106860.

Thanka, M. R., Maheswari, P. U., & Edwin, E. B. (2019). A Hybrid Algorithm for Efficient Task Scheduling in Cloud Computing Environment. International Journal of Reasoning-based Intelligent Systems, 11(2), 134-140.

Mohanty, S., Patra, P. K., Ray, M., & Mohapatra, S. (2021). A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing. In Research Anthology on Architectures, Frameworks,

and Integration Strategies for Distributed and Cloud Computing (pp. 504-526). IGI Global.

Miao, Z., Yong, P., Mei, Y., Quanjun, Y., & Xu, X. (2021). A Discrete PSO-based Static Load Balancing Algorithm for Distributed Simulations in a Cloud Environment. Future Generation Computer Systems, 115, 497-516.

Pradhan, A., & Bisoy, S. K. (2022). A Novel Load Balancing Technique for Cloud Computing Platform Based on PSO. Journal of King Saud University-Computer and Information Sciences,

(7), 3988-3995.

Liu, Q., Zeng, L., Bilal, M., Song, H., Liu, X., Zhang, Y., & Cao, X. (2023). A Multi-Swarm PSO Approach to large-Scale Task Scheduling in a Sustainable Supply Chain Datacenter. IEEE Transactions on Green Communications and Networking.

Al Reshan, M. S., Syed, D., Islam, N., Shaikh, A., Hamdi, M., Elmagzoub, M. A., ... & Talpur, K. H. (2023). A Fast Converging And Globally Optimized Approach for Load Balancing in Cloud

Computing. IEEE Access, 11, 11390-11404.

Ghafir, S., Alam, M. A., Siddiqui, F., & Naaz, S. (2024). Load Balancing in Cloud Computing Via Intelligent PSO-Based Feedback Controller. Sustainable Computing: Informatics and

Systems, 41, 100948.

Mohapatra, S., Mohanty, S., Nayak, H. K., Mallick, M. K., Ramesh, J. V. N., & Dudekula, K. V. (2024). DPSO: A Hybrid Approach for Load Balancing using Dragonfly and PSO Algorithm in Cloud Computing Environment. EAI Endorsed Transactions on Internet of Things, 10.