Ketersediaan Tinggi Website menggunakan Penskalaan Otomatis & Penyeimbang Beban AWS

Authors

  • Haeruddin Haeruddin Universitas Internasional Batam
  • Andik Yulianto
  • Stefanus Eko Prasetyo
  • Gautama Wijaya
  • Dominggo Givarel

Abstract

The rapid development of information technology requires digital service systems to have high performance and optimal availability. One of the main challenges in managing web-based services is the system's ability to deal with an unpredictable surge in the number of users. If not anticipated, this can cause a decrease in performance and even detrimental downtime. This study aims to analyze and implement the concept of auto scaling and load balancing in an effort to improve the performance and availability of web-based services. Auto scaling functions to automatically adjust the number of server resources according to workload needs, while load balancing plays a role in distributing network traffic evenly to several servers. The research method used is an experiment, by implementing and testing service performance before and after the implementation of auto scaling and load balancing. The test results show that the combination of the two technologies is able to increase the speed of service response, reduce server load, and maintain optimal service availability. This research is expected to be a reference in the development of reliable and efficient cloud-based systems.

References

1. Mell, P., & Grance, T. (2011). The NIST Definition of Cloud Computing. National Institute of Standards and Technology. Special Publication 800-145. https://doi.org/10.6028/NIST.SP.800-145

2. Rittinghouse, J. W., & Ransome, J. F. (2017). Cloud Computing: Implementation, Management, and Security. CRC Press.

3. Erl, T., Mahmood, Z., & Puttini, R. (2013). Cloud Computing: Concepts, Technology & Architecture. Prentice Hall.

4. Amazon Web Services. (2023). About AWS. Retrieved from https://aws.amazon.com/about-aws/

5. Amazon Web Services. (2023). Amazon EC2 – Virtual Servers in the Cloud. Retrieved from https://aws.amazon.com/ec2/

6. Amazon Web Services. (2023). Amazon Virtual Private Cloud (VPC). Retrieved from https://aws.amazon.com/vpc/

7. Gaur, M., & Sharma, A. (2019). Enhancement of Auto Scaling and Load Balancing using AWS.Dalam penelitian ini, AWS digunakan untuk membandingkan performa sistem sebelum dan sesudah penerapan auto scaling dan load balancer. Hasil menunjukkan penurunan response time hingga47%.https://www.researchgate.net/publication/338833518

8. Chawla, K. (2024). Adaptive Load Balancing in Cloud Computing Using Reinforcement Learning.Studi ini menggunakan pendekatan pembelajaran penguatan untuk meningkatkan efisiensi load balancing dalam infrastruktur cloud dinamis.https://arxiv.org/abs/2409.04896

9. Jin, Y., & Yang, S. (2025). Scalable Deep Learning-Based Auto Scaling for AI Inference in Cloud.Pendekatan deep learning digunakan untuk memprediksi trafik layanan AI dan menyesuaikan kapasitas secara dinamis.https://arxiv.org/abs/2504.15296

10. Shuja, J., Bilal, K., Hayat, K., Madani, S.A., Khan, S.U. and Shahzad, S. (2012) Energy-Efficient Data Centers. Computing, 94, 973-994.

11. Heba Nashaat, Nesma Ashry, and Rawya Rizk. 2019. Smart elastic scheduling algorithm for virtual machine migration in cloud computing. J. Supercomput. 75, 7 (July 2019), 3842–3865. https://doi.org/10.1007/s11227-019-02748-2

12. AWS Blog (2022). Scaling Strategies for Elastic Load Balancing.Membahas teknik sharding ELB dan metode penskalaan ALB untuk skenario skala besar di production environment.https://aws.amazon.com/blogs/networking-and-content-delivery/scaling-strategies-for-elastic-load-balancing

13. Haeruddin, H. (2023, March 20). Ketersediaan Tinggi Infrastruktur Elearning Berbasis Komputasi Awan. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 10(1), 670-682. https://doi.org/https://doi.org/10.35957/jatisi.v10i1.5050

14. Ramegowda, Ashalatha & Agarkhed, Jayashree. (2015). Evaluation of Auto Scaling and Load Balancing Features in Cloud. International Journal of Computer Applications. 117. 30-33. 10.5120/20561-2949.

15. M. Mangayarkarasi, S. Tamil Selvan, R. Kuppuchamy, S. Shanthi, and S. R. Prem, “Highly Scalable and Load Balanced Web Server On AWS Cloud,” IOP Conf. Ser. Mater. Sci. Eng., Vol. 1055, No. 1, p. 012113, 2021, doi: 10.1088/1757-899x/1055/1/012113.

16. T. Lorido-Botran, J. Miguel-Alonso, and J. A. Lozano, “A Review of Auto-Scaling Techniques for Elastic Applications in Cloud Environments,” J. Grid Comput., Vol. 12, No. 4, pp. 559–592, 2014, doi: 10.1007/s10723-014-9314-7.

17. Shahid, M. A., Alam, M. M., & Su’ud, M. M. (2025). A Comprehensive Analysis of Load Balancing in Cloud Computing: Examining Methodologies and Research Practices for an Effective Hybrid Approach. https://doi.org/10.21203/rs.3.rs-6453751/v1

Published

2025-07-23

How to Cite

Haeruddin, H., Andik Yulianto, Stefanus Eko Prasetyo, Gautama Wijaya, & Dominggo Givarel. (2025). Ketersediaan Tinggi Website menggunakan Penskalaan Otomatis & Penyeimbang Beban AWS . Telcomatics, 10(1). Retrieved from https://journal.uib.ac.id/index.php/telcomatics/article/view/10951

Issue

Vol. 10 No. 1 (2025): In Press

Section

Articles

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Copyright (c) 2025 Telcomatics

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This work is licensed under a Creative Commons Attribution 4.0 International License.