A Cloud-RAN based end-to-end computation offloading in Mobile Edge Computing

Abstract

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.