The rapid expansion of cloud applications has led to unprecedented increases in network traffic volume, diversity, and complexity. As Cloud Service Providers (CSPs) adopt decentralized, geographically distributed data centers, effective traffic management across these environments has become critical. Distributed Rate Limiting (DRL) has emerged as an essential tool to manage the complex traffic dynamics of decentralized networks, yet traditional centralized rate limiting methods fall short, facing limitations in scalability, adaptability to bursty traffic, and efficiency. This paper presents C3PDAR (Cloud Control with Constant Probabilities and Dynamic Adjustment Range), a novel DRL algorithm tailored for decentralized cloud infrastructures. C3PDAR introduces three key innovations: (1) CPS-BPS DualPoint Rate Limiting and Parent-Child Token Bucket mechanisms, which effectively mitigate burst traffic and short-lived connections while improving bandwidth fairness and inter-tenant isolation; (2) A vSwitch-CGW Cascade Rate Limiting architecture, which reduces CPU overhead in CGW clusters and accelerates convergence by 42%–78%; (3) Virtual Extensible Local Area Network (VXLAN) Padding scheme, which embeds rate-limiting information in existing traffic instead of transmitting new data packets, reducing the communication overhead of the C3PDAR algorithm by over 40%. By integrating these advancements, C3PDAR delivers a scalable, robust solution that outperforms traditional DRL approaches in performance, fault tolerance, and resource efficiency. C3PDAR uniquely empowers CSPs to manage complex, high-volume traffic dynamics in decentralized cloud environments, offering both theoretical insights and practical optimizations for next-generation network control.