Cloud Radio Access Network (C-RAN) and Mobile Edge Computing (MEC) have recently emerged as promising leading technologies for next generation mobile networks. Due to its low access latency, MEC is not only a convenient candidate for deployment of C-RAN, but it can also be served by Mobile Users (MUs) to offload their computation-intensive applications. This convergence can facilitate the utilization of knowledge acquired through inter-BBU information sharing to improve the quality of offloading decision. In this paper, we propose an end-to-end communication and computation offloading architecture which takes the full advantage of C-RAN to solve the MEC offloading problem with regard to both partitioning as well as sending and return RRH assignment problems. Based on the proposed architecture, we model the end-to-end offloading problem as an ILP with the objective of minimizing the cost of offloading considering the intra and inter cluster handover costs besides other factors. Due to the complexity of the end-to-end offloading problem, we propose a combination of utility functions and modified min-cut algorithms to solve the aforementioned problems in a timely manner. Simulation results demonstrate that the proposed approach outperforms significantly other alternatives in terms of execution time, energy consumption and aggregated cost under scenarios with different amounts of normalized throughput, invocation data and workload.

云无线电接入网（C-RAN）和移动边缘计算（MEC）最近已成为下一代移动网络的有前途的领先技术。由于其低访问延迟，MEC不仅是部署C-RAN的方便候选者，而且移动用户（MU）还可为其服务，以减轻其计算密集型应用程序的负担。这种融合可以促进通过BBU间信息共享获得的知识的利用，从而提高卸载决策的质量。在本文中，我们提出了一种端到端的通信和计算分流架构，该架构充分利用C-RAN来解决关于分区以及发送和返回RRH分配问题的MEC分流问题。根据建议的架构，我们将端到端卸载问题建模为ILP，其目的是在考虑其他因素的同时，考虑群集内和群集间的切换成本，以最大程度地降低卸载成本。由于端到端卸载问题的复杂性，我们提出了效用函数和改进的最小割算法的组合，以便及时解决上述问题。仿真结果表明，在具有不同数量的标准化吞吐量，调用数据和工作负载的情况下，该方法在执行时间，能耗和总成本方面均明显优于其他方法。我们提出了效用函数和改进的最小割算法的组合，以及时解决上述问题。仿真结果表明，在具有不同数量的标准化吞吐量，调用数据和工作负载的情况下，该方法在执行时间，能耗和总成本方面均明显优于其他方法。我们提出了效用函数和改进的最小割算法的组合，以及时解决上述问题。仿真结果表明，在具有不同数量的标准化吞吐量，调用数据和工作负载的情况下，该方法在执行时间，能耗和总成本方面均明显优于其他方法。