Abstract:

Virtualization resource computing and load allocation in a Cloud-Radio Access Network (C-RAN) is studied. First, based on the C-RAN architecture, a system model is proposed as a virtualization evolution to incorporate all factors influencing computing resource usage. The system model includes user and traffic models, wireless network models, computing resource usage models, and overload prevention mechanisms. Second, two advanced heuristic allocation methods are proposed to allocate User Processing (UP) to the computing unit-Baseband Unit (BBU). This allocation should only occur when each user terminal arrives at the system. The impact of spatial user distribution on the utilization of virtual computing resources is also studied. Finally, by pooling resources, long-term load balancing is achieved while adapting to short-term load fluctuations caused by traffic changes and scheduling effects. Based on the system-level simulation results and considering the average processing load, the proposed heuristic allocation method is shown to have a significantly better overload performance and user experience than those of classical heuristic static allocation and heuristic random allocation methods. Even when it impacts the user experience, the proposed method can conserve 57% of computing resources compared to that by other methods.

Key words: Cloud-Radio Access Network (C-RAN), virtualized computing resource, traffic model, Baseband Unit (BBU), heuristic allocation, overload prevention mechanism, admission control discard rate

摘要：

针对云无线接入网(C-RAN)中的虚拟化资源计算和负载分配问题进行研究。首先，在C-RAN架构的基础上，提出一种作为虚拟化演进的系统模型，以捕捉关于计算资源使用的所有影响因素，该系统模型包括用户和流量模型、无线网络模型、计算资源使用模型以及过载预防机制；其次，提出2种先进的启发式分配方法，分配用户处理(UP)作业给计算单元-基带单元(BBU)，且只在各个用户终端到达系统时才将UP分配给BBU，并研究了空间用户分布对于所利用的虚拟计算资源的影响；最后，通过池化处理资源，实现长期的负载均衡，同时适应由于流量变化和调度效应造成的短期负载波动。基于系统级的仿真结果表明，在考虑平均处理负载的情况下，所提启发式分配方法的过载性能和用户体验明显优于经典的启发式静态分配方法和启发式随机分配方法，即使在对用户体验有一定影响的情况下，该启发式方法也可节省57%的计算资源。

关键词: 云无线接入网, 虚拟化计算资源, 流量模型, 基带单元, 启发式分配, 过载预防机制, 准入控制丢弃率