Social-Aware Edge Caching in Double-layer distributed Fog Radio Access Networks

Fog Computing is a new variety of the cloud computing paradigm that brings virtualized cloud services to the edge of the network to control the devices in the IoT. Fog computing is implemented as an intermediate platform between end devices and cloud computing data centers. The recent popularity of the Internet of Things (IoT) has made fog computing a necessity to handle a variety of devices. It has been recognized as an important platform to provide efficient, location aware, close to the edge, cloud services. Fog radio access network(F-RAN) is a promising network architecture derived from fog computing. In this paper, we propose a device-to-device two-layer distributed cache model based on fog computing. First, we proposed a double-layer distributed cache model based on D2D communication and fog wireless access network, then we made one of the user equipments(DEs) to be central UE. The central UE can actively cache all the content of the fog node. When a UE applies for a file, it first applies to the central UE. If the central UE does not have one, it then applies to neighboring UEs in turn according to the priority of the sum of local content popularity which can reduce transmission delay. After a period of time, the buffer capacity in each UE is full. When there are new files to be cached, we propose a dynamic programming algorithm based on the knapsack problem, so that the files in each UE always have the largest sum of local content popularity. The simulation results show that this caching method can effectively reduce the transmission delay and the algorithm can improve the cache hate rate of files found in UE.