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Systematic Resource Allocation in Cloud RAN With Caching as a Service Under Two Timescales
Journal article   Peer reviewed

Systematic Resource Allocation in Cloud RAN With Caching as a Service Under Two Timescales

Jianhua Tang, Tony Q. S. Quek, Tsung-Hui Chang and Byonghyo Shim
IEEE transactions on communications, Vol.67(11), pp.7755-7770
01/11/2019

Abstract

alternating direction method of multipliers (ADMM) Array signal processing Baseband C-RAN caching as a service Cloud computing Convex functions Resource management semidefinite relaxation (SDR) Servers two timescales Wireless communication
Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CC&C) together. In this paper, we dissect the interactions of CC&C in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CC&C.

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