Realization of a computational efficient BBU cluster for cloud RAN

Abstract

Computational efficiency, CE, of a baseband unit, BBU, has become a key issue for a cloud radio access network, CRAN. The CE is defined as the sum of computational power, CP and computing resources, CR. Where CP and CR can be defined as the amount of power and processing resources of the core computing unit, CCU, of the BBU consumed during execution of traffic requests from remote radio heads, RRHs, respectively. A CCU is nothing but a general-purpose processor. To optimize CR utilization, less number of BBUs has to execute the same amount of RRH-requests. This causes dramatic rise in CCU’s operating temperature and imposes CP efficiency problem, because leakage power of a CCU, which is a wasted power, increases with its operating temperature. On the other hand, a CCU can reduce its CP remarkably by executing the RRH-requests slowly at lower frequency. However, the execution has to meet deadline of the RRH-request that is equivalent to a circuit delay in CCU, which depends on supply voltage, CCU-temperature, and frequency. Therefore, it is very challenging to minimize CP of GPP while optimizing CR subject to RRH-request’s deadline. Previous approaches, including our previously proposed TADCRA scheme, tries to turn off as many GPPs as possible by integrating traffic load on fewer GPPs to maximize the CR utilization while working at allowable temperature. Compared to TADCRA, it is observed that significant CP can be saved from the active GPP by dynamically adjusting its voltage and frequency (DVFS) while executing RRH-requests. Thus, given optimal number of GPPs obtained from TADCRA that maximizes the CR efficiency, a computational efficiency problem is formulated subject to minimum operating frequency, voltage and RRH-request’s deadline constraints. To address this problem, we propose computational efficient allocation (CEA) algorithm, where Lagrange multiplier tool is used for CP efficiency problem, while our proposed heuristic, Win–Win, solves the CR optimization problem subject to RRH-request’s deadline. Simulation results substantiate the proposed algorithm can save 16%, 29% and 46% more CP than TADCRA, LDA and conventional methods, respectively. The CEA increases CR utilization rate by 15% and 45% from LDA and conventional schemes, respectively, with significantly less number of CCUs requirements. Moreover, Win–Win heuristic satisfies the deadline constraint compared to first fit decreasing, FFD, approach, which misses deadline by 1 ms.