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Cloud radio access network is one of the most promising cellular networks for the next generation of mobile networks. The basic idea of cloud RAN (radio access network) is virtualizing and centralizing the intelligent part of the base station, the base band unit, and keeping remote radio heads on cell site enabling a centralized processing and management. Offloading data computation to edge cloud was proposed as a solution to deal with resource limitation while keeping a good quality of service. In this paper, we propose a strategy to jointly handle offloading decision and offloading request scheduling in cloud RAN. We aim to improve network quality of service while reducing the scheduling cost expressed in terms of overload, network delay, and migration cost. Numerical results show that the proposed approach is able to reduce the response time of the applications, mobile terminal energy consumption, and total execution cost.

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With the increase of data traffic in the global mobile network, the limitation in resources of data computing close to the edge is becoming an important issue to resolve. This paper addresses the cloud radio access network (CRAN) in 5G HetNets architecture and proposes to take benefit of extra computing and storage resources in the edge to enable the offloading of a set of mobile user services from the remote cloud computing servers to an edge cloud computing infrastructure deployed next to remote radio heads (RRHs) to better serve mobile users and improve energy efficiency. However, this architecture poses many challenges. The first one is related to the clustering of the various deployed RRH to better serve end-users. For that, we propose a two-stage RRH clustering mechanism in order to fully exploit the benefits of C-RAN architecture. The second challenge is related to the scheduling of the offloading. Therefore, we propose a cost-based scheduling scheme (CBSS) that aims to minimize the scheduling cost while considering resource availability in the infrastructure, resource requirements from users' applications, services execution deadlines, and load balancing. The proposed solution permits us to make better offloading decisions and to improve the users' experiences. The solution was implemented in a simulator to highlight its performances and compare them with other existing approaches.

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