Analysing The Latency Performance Of Distributed Storage Systems: A Study Of Amazon S3 Utilising Real Service Simulation Techniques
DOI:
https://doi.org/10.48047/0p4p3819Keywords:
latency performance, storage systems, amazon simple storage service, real-world scenarios,Abstract
This research effort used real-world service simulation methodologies to investigate the latency performance of distributed storage systems, namely amazon simple storage service (s3). The researcher used amazon s3's distributed storage systems as the independent variable, the latency performance of services as the dependent variable, and the real service simulation approach as the mediating variable. To get performance data that was typical of a wide range of workloads, a quantitative research approach was used, and system random sampling was used as well. The simulation model was able to mimic real-world scenarios, such uploading, retrieving, and deleting items, by employing a broad variety of object sizes and concurrency levels. The average response time, distribution, and tail latency were used to figure out delay. This was done so that time could be measured. This was done to tell the difference between delays that are normal and those that are important. Researchers found that larger items and more simultaneous access caused latency to go up, whereas smaller things had response times that were more stable across different locations. Based on the evidence, this was the conclusion that was made. To better understand how amazon supply service 3 works, real service simulation methods were employed. These methods precisely replicated the parameters observed in the real world. This study has significantly enhanced understanding of the efficiency of distributed storage by introducing fresh information. These findings validate approaches aimed at enhancing operations conducted via cloud computing.
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References
al-qerem, a., alauthman, m., gupta, b., & razaque, a. (2021). Cloud storage performance and security: techniques and challenges. Journal of cloud computing, 10(1), 1-20.
Bao, z., liu, q., huang, x. J., & wei, z. (2025). Sfmss: service flow aware medical scenario simulation for conversational data generation. In findings of the association for computational linguistics: naacl 2025 (pp. 4586-4604).
Chen, j., li, x., & liu, y. (2022). Latency-aware data management in distributed cloud storage systems. Future generation computer systems, 128, 276–286.
Gupta, h., sharma, a., & singh, r. (2020). Performance evaluation of cloud storage systems using real service workloads. International journal of cloud applications and computing, 10(3), 34–47.
Hoang, t. T., pham, l. M., & nguyen, h. S. (2025). Lavp: a latency-aware virtual network function placement strategy for service function chain in network function virtualization. Ieee access.
Kaur, s., & verma, p. (2021). Evaluating performance of amazon s3 under varying workloads. Journal of grid computing, 19(2), 1–18.
Polat, o., oyucu, s., türkoğlu, m., polat, h., aksoz, a., & yardımcı, f. (2024). Hybrid ai-powered real-time distributed denial of service detection and traffic monitoring for software-defined-based vehicular ad hoc networks: a new paradigm for securing intelligent transportation networks. Applied sciences, 14(22), 10501.
Shen, h., zhang, y., & xu, j. (2020). Performance modelling and analysis of large-scale distributed storage systems. Ieee transactions on parallel and distributed systems, 31(12), 2871–2885.
Singh, g. D., tripathi, v., dumka, a., rathore, r. S., bajaj, m., escorcia-gutierrez, j., ... & prokop, l. (2024). A novel framework for capacitated sdn controller placement: balancing latency and reliability with pso algorithm. Alexandria engineering journal, 87, 77-92.
Van damme, s., sameri, j., schwarzmann, s., wei, q., trivisonno, r., de turck, f., & torres vega, m. (2024). Impact of latency on qoe, performance, and collaboration in interactive multi-user virtual reality. Applied sciences, 14(6), 2290.
Zhang, y., & xu, h. (2021). Latency performance challenges in large-scale distributed systems. Journal of cloud computing, 10(1), 1–15.
Zhang, y., wang, c., & huang, j. (2023). Real-time performance analysis of distributed storage in cloud computing environments. Ieee transactions on cloud computing, 11(2), 178–190.
Zhou, l., & zhang, q. (2019). Performance evaluation of cloud storage services with real workloads. Journal of cloud computing, 8(1), 1–13.
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