Site Güvenilirliği Mühendisliği İçgörülerinden Yararlanarak Yerel Bulut İzleme Üzerine Bir Araştırma

Öz

Ölçeklenebilir ve taşınabilir uygulamalara yönelik artan talep, dağıtık sistemler ve bulut bilişimin önemini artırmıştır. Güvenilirlik ve erişilebilirliği sağlamak için Site Reliability Engineering (SRE) yaklaşımı kritik hale gelmiştir. Bu çalışmada, Amazon Web Services (AWS), Google Cloud Platform (GCP) ve Microsoft Azure’un yerel bulut izleme servisleri SRE çerçevesinde karşılaştırılmıştır. Golang tabanlı, mikroservis mimarili, API Gateway ve olay odaklı desenleri kullanan bir PaaS uygulaması geliştirilmiş; ilişkisel veritabanları ve bulut-yerel mesajlaşma servisleriyle desteklenmiştir. İzleme araçları, sistem sağlığını değerlendirmek üzere üçüncü taraf çözümlerle birlikte entegre edilmiştir. Analiz; çeşitlilik, maliyet, uyarı gecikme süresi, veri toplama sıklığı ve saklama süresi ölçütlerine odaklanmıştır. Bulgular, GCP’nin ayrıntılı sanal makine metrikleri sunarak ek yapılandırmaya ihtiyaç duymadığını ve olay odaklı mimarilerde en yüksek mesaj işleme kapasitesine sahip olduğunu göstermektedir. Çalışma, güvenilirlik odaklı dağıtımlar için platformların izleme yeteneklerine ilişkin karşılaştırmalı içgörüler sunmaktadır.

Anahtar Kelimeler

• İzleme Sistemleri
• Site Güvenirliği Mühendilsiği
• Bulut Bilişim
• Yazılım Mimarisi

Destekleyen Kurum

Çalışma, herhangi bir kurum tarafından desteklenmemiştir.

Proje Numarası

Çalışma, herhangi bir proje tarafından desteklenmemiştir.

Etik Beyan

Çalışma, bilimsel etik ilkelerine uygun olarak hazırlanmıştır.

A Research on the Native Cloud Monitoring with the Help of Site Reliability Engineering Insights

Öz

The demand for scalable and portable applications has accelerated the adoption of distributed systems and cloud computing. To ensure reliability and availability, Site Reliability Engineering (SRE) has become essential. This study compares native cloud monitoring services of Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure within the SRE framework. A PaaS-based sample application was developed using Golang, microservice architecture, API Gateway, and event-driven patterns, supported by relational databases and cloud-native messaging services. Providers’ monitoring tools, along with third-party solutions, were integrated to assess system health. Evaluation focused on objective SRE metrics: variety, cost, alert latency, data collection frequency, and retention. Findings show that GCP provides detailed VM-level metrics without additional configuration in API Gateway setups and achieves the highest message-handling capacity in event-driven architectures. The study offers a comparative analysis of monitoring capabilities and insights into platform suitability for reliability-centric deployments.

Anahtar Kelimeler

• Monitoring Systems
• Site Reliability Engineering
• Cloud Computing
• Software Architecture

Destekleyen Kurum

The study was not supported by any institution.

Proje Numarası

Çalışma, herhangi bir proje tarafından desteklenmemiştir.

Etik Beyan

The study was prepared in accordance with scientific ethical principles.

Kaynakça

1. Ker, D. (2021). Measuring cloud services use by businesses (OECD Digital Economy Papers No. 304). OECD Publishing. https://doi.org/10.1787/71a0eb69-en
2. Musa, J. D. (1980). The measurement and management of software reliability. Proceedings of the IEEE, 68(9), 1131–1143. https://doi.org/10.1109/PROC.1980.11812
3. Musa, J. D., & Everett, W. W. (1990). Software-reliability engineering: Technology for the 1990s. IEEE Software, 7(6), 36–43. https://doi.org/10.1109/52.60588
4. Google. (n.d.). What is Site Reliability Engineering (SRE)? https://sre.google
5. Niknejad, N., Ismail, W., Ghani, I., Nazari, B., & Bahari, M. (2020). Understanding service-oriented architecture (SOA): A systematic literature review and directions for further investigation. Information Systems, 91, 101491. https://doi.org/10.1016/j.is.2019.101491
6. İ. K. Aksakalli, T. Çelik, A. B. Can, and B. Tekinerdogan, “Deployment and communication patterns in microservice architectures: A systematic literature review,” Journal of Systems and Software, vol. 180, p. 111014, 2021. doi: 10.1016/j.jss.2021.111014
7. Jhingran, S., & Rakesh, N. (2023). Performance analysis of microservices behavior in cloud vs containerized domain based on CPU utilization. International Journal of Recent Innovations and Trends in Computing and Communication, 11, 509–516. https://doi.org/10.17762/ijritcc.v11i6s.6959
8. Aceto, G., Botta, A., Donato, W., & Pescapè, A. (2013). Cloud monitoring: A survey. Computer Networks, 57, 2093–2115. https://doi.org/10.1016/j.comnet.2013.04.001

9. Ward, J. & Barker, A. (2014). Observing the clouds: A survey and taxonomy of cloud monitoring. Journal of Cloud Computing, 3, Article 24. https://doi.org/10.1186/s13677-014-0024-2
10. Gefen, D. (2002). Customer loyalty in e-commerce. Journal of the Association for Information Systems, 3, 27–51. https://doi.org/10.17705/1jais.00022
11. Dave, D. M. (2023). Impact of site reliability engineering on manufacturing operations: Improving efficiency and reducing downtime. International Journal of Scientific and Research Publications, 10, Article 14312. https://doi.org/10.29322/IJSRP.13.11.2023.p14312
12. Kumar, P., Singh, P., Chopra, S., Sarna, J. S., & Rawat, K. (2017). Inspection of cloud computing monitoring tools. In Proceedings of the International Conference on Infocom Technologies and Unmanned Systems (ICTUS) (pp. 361–365). IEEE. https://doi.org/10.1109/ICTUS.2017.8286033
13. Fatema, K., Emeakaroha, V. C., Healy, P. D., Morrison, J. P., & Lynn, T. (2014). A survey of cloud monitoring tools: Taxonomy, capabilities and objectives. Journal of Parallel and Distributed Computing, 74, 2918–2933. https://doi.org/10.1016/j.jpdc.2014.06.007
14. Birje, M. N., & Bulla, C. (2019). Commercial and open source cloud monitoring tools: A review. In M. S. Obaidat & I. Awan (Eds.), Proceedings of the International Conference on E-Business and Telecommunications (pp. 480–490). Springer. https://doi.org/10.1007/978-3-030-24322-7_59
15. Saraswat, M., & Tripathi, R. C. (2020). Cloud computing: Comparison and analysis of cloud service providers—AWS, Microsoft, and Google. In Proceedings of the 9th International Conference on System Modeling & Advancement in Research Trends (SMART) (pp. 281–285). IEEE.
16. Noviani, E. F., Kembara, B., Pratama, B. A. Y., Sari, D. A. P., Shiddiqi, A. M., & Santoso, B. J. (2022). Performance analysis of AWS and GCP cloud providers. In Proceedings of the IEEE International Conference on Cybernetics and Computational Intelligence (pp. 236–241). IEEE. https://doi.org/10.1109/CyberneticsCom55287.2022.9865484
17. Tamburri, D., Miglierina, M., & Di Nitto, E. (2020). Cloud applications monitoring: An industrial study. Information and Software Technology, 127, 106376. https://doi.org/10.1016/j.infsof.2020.106376
18. Calero, J. M. A., & Aguado, J. G. (2015). Comparative analysis of architectures for monitoring cloud computing infrastructures. Future Generation Computer Systems, 47, 16–30.
19. Tanadechopon, T., & Kasemsontitum, B. (2023). Performance evaluation of programming languages as API services for cloud environments: A comparative study of PHP, Python, Node.js, and Golang. In Proceedings of the 7th International Conference on Information Technology (InCIT) (pp. 293–297). IEEE. https://doi.org/10.1109/InCIT60207.2023.10413079
20. Koç, C. (2025). The investigation of native cloud monitoring services with the use of the site reliability engineering approach (Master’s thesis, Çanakkale Onsekiz Mart University, School of Graduate Studies, Department of Computer Engineering). Çanakkale Onsekiz Mart University.
21. Tanenbaum, A. S., & Bos, H. J. (2014). Modern operating systems (4th ed.). Pearson.