This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Further, a cloud-based traffic management is employed to schedule and allocate resources among user job requests according to the associated traffic states to minimize latency and maximize bandwidth utilization. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it is effective in minimizing the worse effect of traffic congestion and improves bandwidth utilization and reduces job execution latency up to 17.07% and 18%, respectively.

翻译：本文提出了一种新颖的云无线接入网络（C-RAN）流量分析与管理模型，该模型通过采用适当的处理机制来预估可能出现的RAN流量拥塞并减轻其影响。引入了一种计算方法，根据不同作业请求的带宽消耗和执行时间，将异构RAN流量分类为不同的流量状态。进而，采用基于云的流量管理机制，根据关联的流量状态在用户作业请求之间调度和分配资源，以最小化延迟并最大化带宽利用率。实验评估及与现有最先进方法的对比表明，该模型在减轻流量拥塞负面影响方面具有有效性，并分别将带宽利用率提升及作业执行延迟降低了17.07%和18%。