Analysis of Load Balancing Least Connection and Shortest Expected Delay Algorithm for Web Server Using Kube-Proxy on Kubernetes




Penelitian ini membandingkan dan menganalisis kinerja algoritma load balancing shortest expected delay dan Least Connection untuk Web Server menggunakan Kube-Proxy di Kubernetes. Peningkatan jumlah pengguna dan perubahan jumlah node pekerja yang mempengaruhi kinerja system menjadi focus penelitian ini. Hasil penelitian menunjukkan bahwa pada parameter elapsed time, shortest expected delay dengan memiliki waktu yaitu 216.295 ms. Dalam waktu pemrosesan server, shortest expected delay lebih baik dengan menghasilkan 214.257ms. Throughput saat menggunakan algoritma shortest expected delay lebih besar, rata-rata 560.256 paket/detik. Algoritma Least Connection lebih baik dengan memiliki efisiensi 35,24% dalam hal penggunaan CPU, dibandingkan shortest expected delay. Meningkatkan klaster dari dua node pekerja menjadi empat node pekerja menghasilkan pengurangan waktu pemrosesan server yang signifikan, yang berarti penyeimbangan beban menggunakan 4 node pekerja lebih efektif.

Kata kunci: komputasi awan, load balancing, Kubernetes, Web Server.



This research compares and analyzes the performance of the load balancing algorithm Shortest Expected Delay and Least Connection for web server using Kube-Proxy on Kubernetes. The increasing number of users and changes in the number of worker nodes that affect system performance are the focus of this research. The results show that in elapsed time parameter, shortest expected delay has the time, which is 216.295ms. In server processing time, the shortest expected delay is better by producing 214.257ms. Throughput when using the shortest expected delay algorithm is greater, an average of 560,256 packets/second. The Least Connection algorithm is better with an efficiency of 35.24% in terms of CPU usage, compared to the shortest expected delay. Upgrading the cluster from two worker nodes to four worker nodes resulted in a significant reduction in server processing time, which meant more effective load balancing using 4 worker nodes.

Keywords: cloud computing, load balancing, Kubernetes, Web Server.

Kata Kunci

cloud computing; load balancing; Kubernetes; Web Server

Teks Lengkap:

PDF (English)


Bhatt, D. (2011). A Revolution in Information Technology - Cloud Computing. Walailak Journal of Science and Technology, 9(2), 107-113.

Choi, J. H., & Kim, S. W. (2021). Cloud-based ATC Platform Architecture Design Using Kubernetes. IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), (pp. 1–2).

Deepa, T., & Cheelu, D. (2017). A comparative study of static and dynamic load balancing algorithms in cloud computing. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), (pp. 3375–3378).

Dewi, L. P., Noertjahyana, A., Palit, H. N., & Yedutun, K. (2019). Server Scalability Using Kubernetes. Technology Innovation Management and Engineering Science International Conference (TIMES-ICON), (pp. 1–4).

Dua, A., Randive, S., Agarwal, A., & Kumar, N. (2020). Efficient Load balancing to serve Heterogeneous Requests in Clustered Systems using Kubernetes. Consumer Communications & Networking Conference (CCNC), (pp. 1–2).

Ferreira, A. P., & Sinnott, R. (2019). A Performance Evaluation of Containers Running on Managed Kubernetes Services. IEEE International Conference on Cloud Computing Technology and Science (CloudCom), (pp. 199–208).

Gawel, M., & Zielinski, K. (2019). Analysis and Evaluation of Kubernetes Based NFV Management and Orchestration. International Conference on Cloud Computing (CLOUD), (pp. 511–513).

He, Z. (2020). Novel Container Cloud Elastic Scaling Strategy based on Kubernetes. Information Technology and Mechatronics Engineering Conference (ITOEC) , (pp. 1400–1404).

Hidayah, I., Rendy Munadi, & Indrarini Dyah Irawati. (2019). Implementasi High-availability Web Server Menggunakan Load Balancing As A Service Pada Openstack Cloud. EProceedings of Engineering, 6 (3), 10278.

Hu, T., & Wang, Y. (2021). A Kubernetes Autoscaler Based on Pod Replicas Prediction. 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS), (pp. 238–241).

Kaur, S., & Sharma, T. (2018). Efficient load balancing using improved central load balancing technique. International Conference on Inventive Systems and Control (ICISC), (pp. 1–5).

Korobeinikova, T., Maidaniuk, V., Romanyuk, O., Chekhmestruk, R., Romanyuk, O., & Romanyuk, S. (2022). Web-applications Fault Tolerance and Autoscaling Provided by the Combined Method of Databases Scaling. International Conference on Advanced Computer Information Technologies (ACIT), (pp. 27–32).

Kumar, S., & Rana, D. S. (2015). Various Dynamic Load Balancing Algorithms in Cloud Environment: A Survey. International Journal of Computer Applications, 129(6), 14-19.

Li, R. (2019). Load balancing strategies in Kubernetes. ITNEXT. Retrieved from

Lina, L., Longguo, L., & Xinyu, Y. (2000). An agent-based load balancing mechanism: PLRM using Java. International Conference on Technology of Object-Oriented Languages and Systems, (pp. 176–181).

Lui, J. C. S., Muntz, R. R., & Towsley, D. (1995). Bounding the mean response time of the minimum expected delay routing policy: an algorithmic approach. IEEE Transactions on Computers, 44(12), 1371–1382.

Muddinagiri, R., Ambavane, S., & Bayas, S. (2019). Self-Hosted Kubernetes: Deploying Docker Containers Locally With Minikube. International Conference on Innovative Trends and Advances in Engineering and Technology (ICITAET), (pp. 239–243).

Mudrikah, F. Z. A., Istikmal and B. Aditya. (2022). Design of a Geographic Information System for Forest and Land Fires Based on a Real-Time Database on Microservices Infrastructure. International Conference on Internet of Things and Intelligence Systems (IoTaIS), (pp. 1-6).

Nakagawa, G., & Oikawa, S. (2016). Behavior-Based Memory Resource Management for Container-Based Virtualization. Intl Conf on Applied Computing and Information Technology-Intl Conf on Computational Science-Intelligence and Applied Informatics-Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD), (pp. 213–217).

Nancy, J. J., Mani S., T., Rohith, S., Saranraj, S., & Vigneswaran, T. (2020). Load Balancing using Load Sharing Technique in Distribution System. International Conference on Advanced Computing and Communication Systems (ICACCS) , (pp. 791–794).

Portworx, & Security Aqua. (2019). Container Adoption Survey. Retrieved from

Raj, P., Vanga, S., & Chaudhary, A. (2022). Setting up a Kubernetes Cluster using Azure Kubernetes Service. In Cloud-native Computing: How to Design, Develop, and Secure Microservices and Event-Driven Applications (pp. 203–221). John Wiley & Sons, Inc.

Rao, S. K. N., Paganelli, F., & Morton, A. (2021). Benchmarking Kubernetes Container-Networking for Telco Usecases. IEEE Global Communications Conference (GLOBECOM), (pp. 1–7).

Sivagami, V. M., & K.S.EaswaraKumar. (2018). Performance analysis of Load balancing algorithms using LBaaS. International Journal Of Research And Analytical Reviews, 9(4), 140–150.

Tian, J. S., Huang, S.-C., Yang, S.-R., & Lin, P. (2022). Kubernetes Edge-Powered Vision-Based Navigation Assistance System for Robotic Vehicles. International Wireless Communications and Mobile Computing (IWCMC), (pp. 457–462).

Zhu, L., Cui, J., & Xiong, G. (2018). Improved dynamic load balancing algorithm based on Least-Connection Scheduling. Information Technology and Mechatronics Engineering Conference (ITOEC), (pp. 1858–1862).



  • Saat ini tidak ada refbacks.


ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik :

Statistik Pengunjung

Free counters!


Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License