In advertising platforms, operations such as real-time bidding, creative retrieval, user profile updates, and performance attribution occur frequently, leading to issues such as sudden traffic surges or cascading effects between links. Therefore, effective resource scheduling and elastic scaling for cloud-native governance are crucial. Based on recent research on cloud-native elastic computing and microservice scheduling, this paper constructs a collaborative optimization framework consisting of five parts: demand forecasting, layered scheduling, service-level scaling, node-level scaling, and overload protection, targeting the load characteristics of five stages in the advertising request chain: access, retrieval, ranking, billing, and logging. Methodologically, the paper proposes multi-metric demand forecasting, SLO constraints for replica control, resource fragmentation penalties, and cold start suppression, and provides validation using publicly available industry data and recent research findings. The research reveals that relying solely on CPU thresholds to control reactive scaling can result in peak-hour latency and off-peak idleness. A combined approach of forecasting first, feedback second, and layered scheduling better meets the millisecond-level service requirements of advertising systems. This provides a more engineering-feasible balance between stability, cost, and delivery for cloud-native advertising platforms.

Linlin Sun. Resource Scheduling and Elastic Scaling Optimization of Distributed Advertising Systems in Cloud-Native Environments. Socio-Economic Statistics Research (2026), Vol. 7, Issue 1: 77-86. https://doi.org/10.38007/SESR.2026.070109.

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