Maximum Profit of Real-Time IoT Content Retrieval by Joint Content Placement and Storage Allocation in C-RANs
Chia‐Cheng Hu, Jeng‐Shyang Pan · IEEE Transactions on Cloud Computing · 2020
In Cloud Radio Access Networks (C-RANs), the performance of real-time Internet of Things (IoT) content retrieval is improved by placing/requesting the contents into/from remote radio heads (RRHs) and baseband units (BBUs). In the previous studies for the problem in jointly placing user contents and allocating storage allocation of RRHs and BBUs, a strategy of minimizing system resource consumption or transmission delay was used. In this article, we adopt a distinct strategy to maximize the profit of content retrieval services under the constraints of meeting the real-time requirements of users and the limited system resources in C-RANs. The problem is formulated as integer linear programming (ILP). Then, an algorithm for solving the ILP is proposed, and it can provide an approximate solution close to the optimal one with a bounded factor. In the simulation conducted, the results verified the above claims. Further, another algorithm is proposed to effectively expand the storage budgets in C-RANs. By controlling a bounded factor, not only the system performance is guaranteed, but also the upper limit of the expanded storage budget is provided. The smaller the bounding factor, the stricter the performance guarantee, but the expected storage budget will increase.