ArticleOpen Access http://dx.doi.org/10.26855/ea.2026.09.009
Optimization Architecture Design of Distributed Systems for Advertising Technology Platforms in High-concurrency Scenarios
Linlin Sun
The Andrew and Erna Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA.
*Corresponding author: Linlin Sun
Published: July 24, 2026
Abstract
In high-concurrency advertising deployment environments, advertising technology platforms simultaneously bear multiple pressures including sudden request surges, intensive real-time decision-making, concentrated hot data access, and fluctuating link quality. These pressures can lead to response delays, resource contention, cache imbalance, and localized service jitter, thereby narrowing the platform’s stable operating boundaries. Focusing on the business characteristics of short-term high-frequency advertising request processing, intensive state reading, and strict decision time limits, this paper constructs a distributed optimization architecture consisting of traffic access decoupling, multi-level cache collaboration, asynchronous link pressure reduction, and elastic governance. Through pressure testing, metric comparison, and scenario-based operational data, the system’s throughput capacity, latency performance, cache hit rate, and anomaly isolation capabilities are analyzed. Results show that the optimized architecture demonstrates stronger support in peak traffic carrying capacity, tail latency convergence, resource scheduling stability, and fault recovery efficiency, providing technical references for the stable operation and continuous evolution of advertising technology platforms in high-concurrency scenarios.
Keyword
High concurrency; advertising technology platform; distributed system; architecture optimization; elastic governance
References
[1] Dean J, Barroso LA. The tail at scale. Commun ACM. 2013;56(2):74-80.
[2] Nishtala R, Fugal H, Grimm S, et al. Scaling Memcache at Facebook. In: Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI). 2013:385-398.
[3] Estrada-Jiménez J, Parra-Arnau J, Rodríguez-Hoyos A, et al. On the regulation of personal data distribution in online advertising platforms. Eng Appl Artif Intell. 2019;82:13-29.
[4] Christopher RM, Park S, Han SP, et al. Bypassing performance optimizers of real time bidding systems in display ad valuation. Inf Syst Res. 2022;33(2).
[5] Burns B, Beda J, Hightower K. Kubernetes: up and running. 2nd ed. O'Reilly Media; 2019.
[6] Burns B, Oppenheimer D. Design patterns for container-based distributed systems. In: Proceedings of the 8th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud). 2016.
[7] Sriram K, Poojary AA, Jawa V, et al. Return on investment and return on ad spend at the action level of AIDA using last touch attribution method on digital advertising platforms. Int J Internet Mark Advert. 2022;17(1-2):111-132.
[8] Isaenko E, et al. Research of social media channels as a digital analytical and planning technology of advertising campaigns. IOP Conf Ser Mater Sci Eng. 2020;986(1):012014.
Copyright
© 2026 by the author(s).
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license, which permits non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited and is not modified or adapted.
https://creativecommons.org/licenses/by-nc-nd/4.0/
How to cite this paper
Optimization Architecture Design of Distributed Systems for Advertising Technology Platforms in High-concurrency Scenarios
How to cite this paper: Linlin Sun. (2026). Optimization Architecture Design of Distributed Systems for Advertising Technology Platforms in High-concurrency Scenarios. Engineering Advances, 6(3), 177-181.
DOI: http://dx.doi.org/10.26855/ea.2026.09.009