Implementasi Layer-7 Load Balancing Dengan Sistem Geo-Aware Failover Untuk Meningkatkan Reliabilitas Layanan Berbasis Web
DOI:
https://doi.org/10.55826/jtmit.v4i4.1399Keywords:
Layer 7, Load Balancer, Layer-7 Load Balancer, GeoAware, EWMA, Failover, FailbackAbstract
Sistem load balancing merupakan komponen penting dalam menjaga kinerja dan ketersediaan layanan pada infrastruktur jaringan berskala besar, namun banyak implementasi yang masih menggunakan algoritma statis seperti Round Robin yang belum mampu menyesuaikan kondisi kesehatan server dan latensi jaringan secara real-time, sehingga berpotensi meningkatkan latensi dan downtime ketika salah satu server mengalami gangguan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem Layer-7 Load Balancer berbasis Geo-Aware Failover dengan perhitungan Exponential Weighted Moving Average (EWMA) sebagai solusi untuk meningkatkan stabilitas distribusi beban, reliabilitas layanan, serta pengambilan keputusan adaptif pada lingkungan multi-region. Implementasi dilakukan menggunakan OpenResty (Nginx + LuaJIT) yang dilengkapi modul deteksi lokasi pengguna, pemeriksaan kesehatan backend, dan pembaruan nilai latensi secara berkelanjutan. Pengujian performa dan reliabilitas dilakukan menggunakan Apache JMeter dan PyTest dalam skenario normal, failover, dan failback. Hasil pengujian menunjukkan bahwa sistem mampu mempertahankan waktu respons rata-rata pada rentang 208–300 ms, transisi failover kurang dari 1 detik, serta tingkat ketersediaan layanan di atas 99%, dengan peningkatan performa yang lebih baik dibandingkan metode Round Robin konvensional. Dengan demikian, penelitian ini memberikan kontribusi berupa pengembangan mekanisme load balancing adaptif berbasis lokasi dan latensi yang dapat meningkatkan performa, kontinuitas layanan, dan kemampuan adaptif pada arsitektur layanan berbasis web berskala global.
References
[1] H. Tussyadiah, R. M. Negara, and D. D. Sanjoyo, “Distributed gateway-based load balancing in software defined network,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 5, pp. 2352–2361, 2020, doi: 10.12928/TELKOMNIKA.V18I5.14851.
[2] F. Tapia, M. ángel Mora, W. Fuertes, H. Aules, E. Flores, and T. Toulkeridis, “From monolithic systems to microservices: A comparative study of performance,” Appl. Sci., vol. 10, no. 17, 2020, doi: 10.3390/app10175797.
[3] R. Tsyganok, “Methodology for Building Scalable Microservice Architectures on Go for High-Load E-Commerce Platforms,” Univers. Libr. Eng. Technol., vol. 01, no. 02, pp. 42–46, 2024, doi: 10.70315/uloap.ulete.2024.0102007.
[4] S. P. Sitorus, E. R. Hasibuan, and R. Rohani, “Analysis performance of content delivery network by used Rateless Code method,” Sinkron, vol. 7, no. 4, pp. 2348–2359, 2022, doi: 10.33395/sinkron.v7i4.11651.
[5] Sree Priyanka Uppu, “Content Delivery Networks (CDNs) and live streaming: architecting scalable delivery for high-demand events,” World J. Adv. Res. Rev., vol. 26, no. 2, pp. 2153–2164, 2025, doi: 10.30574/wjarr.2025.26.2.1849.
[6] P. Dymora, M. Mazurek, and B. Sudek, “Comparative analysis of selected open-source solutions for traffic balancing in server infrastructures providing www service,” Energies, vol. 14, no. 22, 2021, doi: 10.3390/en14227719.
[7] K. Prasojo and A. M. F. Fadhlurrahman, “Analisis Performa Load balancing pada Web Server Menggunakan Nginx,” vol. 3, no. 1, pp. 30–39, 2025.
[8] I. Technology, “Evaluation and Comparison of Load Balancing Algorithm Performance in the Implementation of Weighted Least Connections and Round Robin in Cloud Computing Environment,” J. Comput. Sci. Inf. Technol. Telecommun. Eng., vol. 6, no. 1, 2025, doi: 10.30596/jcositte.v6i1.21731.
[9] B. Alankar, G. Sharma, H. Kaur, R. Valverde, and V. Chang, “Experimental setup for investigating the efficient load balancing algorithms on virtual cloud,” Sensors (Switzerland), vol. 20, no. 24, pp. 1–26, 2020, doi: 10.3390/s20247342.
[10] V. Mohammadian, N. J. Navimipour, M. Hosseinzadeh, and A. Darwesh, “Fault-Tolerant Load Balancing in Cloud Computing: A Systematic Literature Review,” IEEE Access, vol. 10, pp. 12714–12731, 2022, doi: 10.1109/ACCESS.2021.3139730.
[11] D. Sharmah and K. C. Bora, “A Survey on Dynamic Load Balancing Techniques in Cloud Computing,” Lect. Notes Electr. Eng., vol. 1088, no. January, pp. 273–282, 2024, doi: 10.1007/978-981-99-6855-8_21.
[12] H. F. Pérez and M. Galicia García, “Journal of Applied Research and Technology,” J. Appl. Res. Technol., vol. 18, pp. 245–258, 2020.
[13] Z. Li, Z. Li, and W. Zhang, “Quality-Aware Task Allocation for Mobile Crowd Sensing Based on Edge Computing,” Electron., vol. 12, no. 4, 2023, doi: 10.3390/electronics12040960.
[14] T. Edge et al., “The Impact of Edge Computing on the Data Center The Impact of Edge Computing on the Data Center”.
[15] D. Agung Rizky Ananta, P. Hari Trisnawan, and K. Amron, “Implementasi Load Balancing Web Server pada Software Defined Networking menggunakan Metode K-Nearest Neighbor (K-NN),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 10, pp. 3598–3606, 2020, [Online]. Available: http://j-ptiik.ub.ac.id
[16] M. C. Saxena, M. Sabharwal, and P. Bajaj, “Review of SDN-based load-balancing methods, issues, challenges, and roadmap,” Int. J. Electr. Comput. Eng. Syst., vol. 14, no. 9, pp. 1031–1049, 2023, doi: 10.32985/ijeces.14.9.8.
[17] O. M. A. Alssaheli, Z. Z. Abidin, N. A. Zakaria, and Z. A. Abas, “Software Defined Network based Load Balancing for Network Performance Evaluation,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 4, pp. 117–124, 2022, doi: 10.14569/IJACSA.2022.0130414.
[18] H. Allam, “Reliability at the Edge: SRE for Distributed Cloud and IoT Platforms,” Int. J. Emerg. Res. Eng. Technol., vol. 6, no. 2, pp. 39–52, 2025, doi: 10.63282/3050-922x.ijeret-v6i2p106.
[19] V. S. Ramdoss, “Optimizing System Performance: Load Balancers and High Availability,” Eastasouth J. Inf. Syst. Comput. Sci., vol. 1, no. 02, pp. 113–117, 2023, doi: 10.58812/esiscs.v1i02.435.
[20] E. J. García Fernández de Castro, E. J. García Puche, and D. Jabba Molinares, “Dynamic Low-Latency Load Balancing Model to Improve Quality of Experience in a Hybrid Fog and Edge Architecture for Massively Multiplayer Online (MMO) Games,” Appl. Sci., vol. 15, no. 12, pp. 1–29, 2025, doi: 10.3390/app15126379.
[21] M. N. A. Rizqi and I. K. Dwi Nuryana, “Analisis Perbandingan Kinerja Algoritma Weighted Round Robin dan Weighted Least Connection Menggunakan Load Balancing Nginx Pada Virtual Private Server(VPS),” J. Informatics Comput. Sci., vol. 4, no. 01, pp. 67–75, 2022, doi: 10.26740/jinacs.v4n01.p67-75.
[22] A. Asghar, A. Abbas, H. A. Khattak, and S. U. Khan, “Fog Based Architecture and Load Balancing Methodology for Health Monitoring Systems,” IEEE Access, vol. 9, pp. 96189–96200, 2021, doi: 10.1109/ACCESS.2021.3094033.
[23] X. Shi, Y. Li, C. Jia, X. Hu, and J. Li, “L7LB: High Performance Layer-7 Load Balancing on Heterogeneous Programmable Platforms,” IEEE INFOCOM 2023 - Conf. Comput. Commun. Work. INFOCOM WKSHPS 2023, 2023, doi: 10.1109/INFOCOMWKSHPS57453.2023.10225882.
[24] A. Syarif Aziz, “Analisis Kinerja Web Server Apache, Nginx, Open Litespeed, Dan Open Resty,” J. Informatics Comput. Sci., vol. 11, no. 1, pp. 1–9, 2025.
[25] D. Aldossary, E. Aldahasi, T. Balharith, and T. Helmy, “A Systematic Literature Review on Load-Balancing Techniques in Fog Computing: Architectures, Strategies, and Emerging Trends,” Computers, vol. 14, no. 6, pp. 1–42, 2025, doi: 10.3390/computers14060217.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Wildy Sheverando, Agustinus Fritz Wijaya
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
