This study focuses on the delay control of real-time advertising recommendation in distributed computing environments, and finds that the parallel processing and memory computing characteristics of the Spark framework can achieve millisecond level response for training and inference of advertising click through rate prediction models. The research background indicates that real-time advertising recommendation needs to quickly adapt to market dynamics, and traditional single machine architectures are difficult to meet the timeliness requirements of massive data processing and complex model training. The research method adopts Spark distributed architecture, combined with random forest algorithm for data preprocessing (missing value removal, normalization, feature selection based on correlation matrix and random forest importance), model training (multi decision tree parallel training and voting mechanism), and inference. Load balancing, RDDs fault-tolerant design, and YARN resource management are used to optimize resource utilization and system stability. The research results found that compared to a single machine environment, a distributed architecture compresses the delay of single sample inference to the millisecond level, significantly shortens the model update cycle, and ensures prediction accuracy; Feature selection effectively reduces redundant information and improves the quality of input data; The parallel deployment of deep learning models (such as LSTM) further captures high-order feature interactions and enhances model accuracy. The conclusion emphasizes that distributed computing technology has built a positive cycle of "low latency, high precision, and strong returns". In the future, Spark Streaming can be used for real-time data stream processing and gradient descent parallelization optimization to promote low latency response throughout the entire chain, and to facilitate the evolution of real-time advertising recommendation systems towards higher precision and lower latency. This provides a reusable technical path for advertisers to achieve precise advertising and improve user experience.

Taige Zhang. Latency Control in Real-Time Advertising Recommendation under Distributed Computing Environments. Distributed Processing System (2025), Vol. 4, Issue 1: 9-16. https://doi.org/10.38007/DPS.2025.040102.

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