Joint Content Placement and Storage Allocation in C-RANs for IoT Sensing Service

Abstract

The Internet of Things (IoT) sensing service allows systems and users to monitor environment states by transmitting the content sensed by a variety of sensors. Owing to billions of sensors and devices deployed in the IoT system, a huge amount of data (big data) are generated, thus injecting tremendous traffic into the network. Cloud radio access network (C-RAN) is a promising wireless network architecture to accommodate the fast growing IoT traffic and improve the performance of IoT services. Caching in C-RAN, which brings content to the edges, not only alleviates the network traffic, thus improving the end-to-end user quality of service (QoS), but also avoids activating the sensors too frequently, thus reducing their energy consumption. The content placement problem determines what and where to cache in C-RAN. However, the caching performance is highly related to the caching storages. The storage allocation problem determines the storage capacities of network entities. In this paper, we jointly optimize the storage allocation problem and content placement problem in a hierarchical cache-enabled C-RAN architecture for IoT sensing service. We formulate the joint problem as an integer linear programming (ILP) model with the objective to minimize the total network traffic cost. The storage allocation problem and content placement problem are constrained by caching storage budgets and cache capacities, respectively. Two heuristic algorithms are proposed in order to reduce the computational complexity of ILP. Extensive simulations have been conducted to demonstrate that the performances of our proposed algorithms approximate the optimal solutions.