Load Balancing Techniques for Server Clustering in Cloud Environment: Systematic Literature Review

Deara Mayanda; Annisa Rizki Amaliah; Muhammad Ridwan Ali Raharja; Nurbojatmiko Nurbojatmiko

doi:10.29103/jreece.v4i2.14906

Load Balancing Techniques for Server Clustering in Cloud Environment: Systematic Literature Review

Deara Mayanda, Annisa Rizki Amaliah, Muhammad Ridwan Ali Raharja, Nurbojatmiko Nurbojatmiko

Abstract

The rapid development of cloud computing has a significant impact on increasing the workload on resources, which is often excessive and a major challenge in computing environments. Load balancing is key to avoid overloading or underloading virtual machines, given the high user demand for service availability. There are several types of load balancing techniques, and this diversity poses its own challenges in selecting the optimal technique to address workload issues. This research presents a systematic literature review with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify various load balancing techniques for server clustering in a cloud computing environment. The purpose of this research is to review previous research on load balancing techniques for server clustering in cloud computing by categorizing based on problems, solutions, research methods, objects, and research results. Research that uses the experimental method will be reviewed again to categorize the research results based on the load balancing matrix, namely response time, make span, resource utilization, migration time, fault tolerance, throughput, and cost. Various publishers, such as IEEE, Elsevier, Springer, Wiley, MDPI and Hindawi were explored as data sources. The research conducted generates more information about load balancing techniques for clustering servers in cloud computing and allows other researchers to fill the current research gap.

Keywords

Cloud Computing; Clustering Server; Load Balancing; Systematic Literature Review

Full Text:

PDF

References

Adil, M., Song, H., Ali, J., Jan, M. A., Attique, M., Abbas, S., & Farouk, A. (2022). Enhanced-AODV: A Robust Three Phase Priority-Based Traffic Load Balancing Scheme for Internet of Things. IEEE Internet of Things Journal, 9(16), 14426–14437. https://doi.org/10.1109/JIOT.2021.3072984

Afzal, S., & Kavitha, G. (2019). Load balancing in cloud computing – A hierarchical taxonomical classification. Journal of Cloud Computing, 8(1), 22. https://doi.org/10.1186/s13677-019-0146-7

Ahmad, M. O., & Khan, R. Z. (2018). Load Balancing Tools and Techniques in Cloud Computing: A Systematic Review (pp. 181–195). https://doi.org/10.1007/978-981-10-3773-3_18

Attallah, S. M. A., Fayek, M. B., Nassar, S. M., & Hemayed, E. E. (2021). Proactive load balancing fault tolerance algorithm in cloud computing. Concurrency and Computation: Practice and Experience, 33(10). https://doi.org/10.1002/cpe.6172

Belgaum, M. R., Musa, S., Alam, M. M., & Su’ud, M. M. (2020). A Systematic Review of Load Balancing Techniques in Software-Defined Networking. IEEE Access, 8, 98612–98636. https://doi.org/10.1109/ACCESS.2020.2995849

Chakrabarti, K., Majumder, K., Sarkar, S., Sing, M., & Chatterjee, S. (2020). Load Balancing Techniques Applied in Cloud Data Centers: A Review (pp. 241–247). https://doi.org/10.1007/978-981-15-2043-3_29

Chinedu, A. D., Ijeoma, C. C., Prof. Inyiama, C., H., Samuel, A., & Okechukwu, O. M. (2022). Review of Hybrid Load Balancing Algorithms in Cloud Computing Environment. ArXiv Preprint. https://doi.org/https://doi.org/10.48550/arXiv.2202.13181

Ebadifard, F., & Babamir, S. M. (2021). Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment. Cluster Computing, 24(2), 1075–1101. https://doi.org/10.1007/s10586-020-03177-0

Gabi, D., Samad, A., & Zainal, A. (2015). Systematic Review on Existing Load Balancing Techniques in Cloud Computing. International Journal of Computer Applications, 125(9), 16–24. https://doi.org/10.5120/ijca2015905539

Gamal, M., Rizk, R., Mahdi, H., & Elnaghi, B. E. (2019). Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing. IEEE Access, 7, 42735–42744. https://doi.org/10.1109/ACCESS.2019.2907615

Gautam, C., Singh, D., & Sharma, A. (2020). ANALYSIS OF LOAD BALANCING ALGORITHMS IN CLOUD COMPUTING. International Journal of Engineering Applied Sciences and Technology, 04(12), 122–126. https://doi.org/10.33564/IJEAST.2020.v04i12.016

Georgopoulos, V. P., Gkikas, D. C., & Theodorou, J. A. (2023). Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020. Sustainability, 15(23), 16385. https://doi.org/10.3390/su152316385

Jena, U. K., Das, P. K., & Kabat, M. R. (2022). Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. Journal of King Saud University - Computer and Information Sciences, 34(6), 2332–2342. https://doi.org/10.1016/j.jksuci.2020.01.012

Kaur, M., & Aron, R. (2021). A systematic study of load balancing approaches in the fog computing environment. The Journal of Supercomputing, 77(8), 9202–9247. https://doi.org/10.1007/s11227-020-03600-8

Kumar, M., & Sharma, S. C. (2020). Dynamic load balancing algorithm to minimize the makespan time and utilize the resources effectively in cloud environment. International Journal of Computers and Applications, 42(1), 108–117. https://doi.org/10.1080/1206212X.2017.1404823

Kushwaha, M., Raina, B. L., & Narayan Singh, S. (2021). Advanced Weighted Round Robin Procedure for Load Balancing in Cloud Computing Environment. 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 215–219. https://doi.org/10.1109/Confluence51648.2021.9377049

Li, G., Yao, Y., Wu, J., Liu, X., Sheng, X., & Lin, Q. (2020). A new load balancing strategy by task allocation in edge computing based on intermediary nodes. EURASIP Journal on Wireless Communications and Networking, 2020(1), 3. https://doi.org/10.1186/s13638-019-1624-9

Mishra, A., & Tiwari, D. (2020). A Proficient Load Balancing Using Priority Algorithm In Cloud Computing. 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 1–6. https://doi.org/10.1109/ICMLANT50963.2020.9355972

Nabi, S., Ibrahim, M., & Jimenez, J. M. (2021). DRALBA: Dynamic and Resource Aware Load Balanced Scheduling Approach for Cloud Computing. IEEE Access, 9, 61283–61297. https://doi.org/10.1109/ACCESS.2021.3074145

Neelima, P., & Reddy, A. R. M. (2020). An efficient load balancing system using adaptive dragonfly algorithm in cloud computing. Cluster Computing, 23(4), 2891–2899. https://doi.org/10.1007/s10586-020-03054-w

Nezami, Z., Zamanifar, K., Djemame, K., & Pournaras, E. (2021). Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things. IEEE Access, 9, 64983–65000. https://doi.org/10.1109/ACCESS.2021.3074962

Padmavathi, M., Basha, Sk. M., & Krishnaiah, V. V. J. R. (2020). Load Balancing Algorithm to Reduce Make Span in Cloud Computing by Enhanced Firefly Approach. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 896–900. https://doi.org/10.1109/ICESC48915.2020.9155662

Panwar, A., Singh, A., Dixit, A., & Parashar, G. (2022). Cloud Computing and Load Balancing: A Review. 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), 334–343. https://doi.org/10.1109/CISES54857.2022.9844367

Patel, K. D., & Bhalodia, T. M. (2019). An Efficient Dynamic Load Balancing Algorithm for Virtual Machine in Cloud Computing. 2019 International Conference on Intelligent Computing and Control Systems (ICCS), 145–150. https://doi.org/10.1109/ICCS45141.2019.9065292

Priya, V., Sathiya Kumar, C., & Kannan, R. (2019). Resource scheduling algorithm with load balancing for cloud service provisioning. Applied Soft Computing, 76, 416–424. https://doi.org/10.1016/j.asoc.2018.12.021

R, K., G, B., R, K., & Y, V. R. R. (2023). Effective load balancing approach in cloud computing using Inspired Lion Optimization Algorithm. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 6, 100326. https://doi.org/10.1016/j.prime.2023.100326

R.M., S. P., Bhattacharya, S., Maddikunta, P. K. R., Somayaji, S. R. K., Lakshmanna, K., Kaluri, R., Hussien, A., & Gadekallu, T. R. (2020). Load balancing of energy cloud using wind driven and firefly algorithms in internet of everything. Journal of Parallel and Distributed Computing, 142, 16–26. https://doi.org/10.1016/j.jpdc.2020.02.010

Sahoo, K. S., Puthal, D., Tiwary, M., Usman, M., Sahoo, B., Wen, Z., Sahoo, B. P. S., & Ranjan, R. (2020). ESMLB: Efficient Switch Migration-Based Load Balancing for Multicontroller SDN in IoT. IEEE Internet of Things Journal, 7(7), 5852–5860. https://doi.org/10.1109/JIOT.2019.2952527

Sefati, S., Mousavinasab, M., & Zareh Farkhady, R. (2022). Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: performance evaluation. The Journal of Supercomputing, 78(1), 18–42. https://doi.org/10.1007/s11227-021-03810-8

Shafiq, D. A., Jhanjhi, N. Z., & Abdullah, A. (2022). Load balancing techniques in cloud computing environment: A review. Journal of King Saud University - Computer and Information Sciences, 34(7), 3910–3933. https://doi.org/10.1016/j.jksuci.2021.02.007

Shafiq, D. A., Jhanjhi, N. Z., Abdullah, A., & Alzain, M. A. (2021). A Load Balancing Algorithm for the Data Centres to Optimize Cloud Computing Applications. IEEE Access, 9, 41731–41744. https://doi.org/10.1109/ACCESS.2021.3065308

Shahid, M. A., Alam, M. M., & Su’ud, M. M. (2023). Performance Evaluation of Load-Balancing Algorithms with Different Service Broker Policies for Cloud Computing. Applied Sciences, 13(3), 1586. https://doi.org/10.3390/app13031586

Shahid, M. A., Islam, N., Alam, M. M., Su’ud, M. M., & Musa, S. (2020). A Comprehensive Study of Load Balancing Approaches in the Cloud Computing Environment and a Novel Fault Tolerance Approach. IEEE Access, 8, 130500–130526.

https://doi.org/10.1109/ACCESS.2020.3009184

Subramanian, K. S., & Teshite, G. (2022). Performance Evaluation of Novel Random Biased-Genetic Algorithm (NRB-GA): A Hybrid Load Balancing Algorithm in a Cloud Computing Environment. Scientific Programming, 2022, 1–13. https://doi.org/10.1155/2022/3042173

Yu, D., Ma, Z., & Wang, R. (2022). Efficient Smart Grid Load Balancing via Fog and Cloud Computing. Mathematical Problems in Engineering, 2022, 1–11. https://doi.org/10.1155/2022/3151249

Zhang, F., & Wang, M. M. (2021). Stochastic Congestion Game for Load Balancing in Mobile-Edge Computing. IEEE Internet of Things Journal, 8(2), 778–790. https://doi.org/10.1109/JIOT.2020.3008009

Zhang, W.-Z., Elgendy, I. A., Hammad, M., Iliyasu, A. M., Du, X., Guizani, M., & El-Latif, A. A. A. (2021). Secure and Optimized Load Balancing for Multitier IoT and Edge-Cloud Computing Systems. IEEE Internet of Things Journal, 8(10), 8119–8132. https://doi.org/10.1109/JIOT.2020.3042433

DOI: https://doi.org/10.29103/jreece.v4i2.14906