A REINFORCEMENT LEARNING-BASED ARCHITECTURE FOR HIERARCHICAL CONTROL OF API WORKFLOWS IN ENERGY-CONSTRAINED AD SYSTEMS

Modern advertising systems face increasing complexity in API workflow management due to interconnected service dependencies, dynamic resource requirements, and stringent energy efficiency constraints. Traditional workflow orchestration approaches struggle to optimize complex API execution sequences while maintaining energy consumption within operational limits. The heterogeneous nature of advertising workflows, including real-time bidding pipelines, content personalization processes, and analytics aggregation tasks, requires sophisticated control mechanisms that can adapt to varying performance requirements and energy availability. This study proposes a Reinforcement Learning (RL)-based architecture for hierarchical control of API workflows in energy-constrained advertising systems. The framework employs a multi-tier control structure where high-level workflow coordinators manage execution strategies while low-level API controllers optimize individual service performance within energy budgets. Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms enable adaptive workflow control policies that balance execution efficiency with energy consumption across distributed advertising infrastructure. Experimental evaluation using enterprise advertising system traces demonstrates that the proposed architecture achieves 41% improvement in workflow completion rates while reducing energy consumption by 37% compared to traditional orchestration methods. The hierarchical approach successfully manages complex workflow dependencies and energy constraints, resulting in 33% better resource utilization efficiency and 29% reduction in workflow execution latency.