C-RAN架构下参数化优化的BBU池虚拟化电源模型

W. H. Al-Zubaedi, H. Al-Raweshidy
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引用次数: 12

摘要

本文介绍了一种基于虚拟化的云无线接入网(C-RAN)的成本效益方案,该方案是一种用于降低基带单元(BBU池)功耗(PC)的技术。BBU的功能被描述为运行在服务器上的一个软件应用程序,称为虚拟BBU (virtual BBU)。为了评估建议方案,提出了C-RAN架构下BBU池的功率模型。这提供了一个参数化和优化的线性功率模型,涵盖了C-RAN系统中与BBU池中PC分析相关的所有各个方面。此外,本文还提出了一种新的最小Bin Slack (NMBS)算法,该算法结合Bin Packing Method (BPM)来最小化vBBUs的数量。NMBS旨在匹配远程无线电头(RRHs)的基带处理负载的适当数量,并尽可能在所有vBBUs之间实现负载平衡,并关闭其他vBBUs。每个vBBU的基带处理过程划分为N个任务,可由多个vBBU处理,以提供系统灵活性。所提出的体系结构是基于整数线性规划来寻找vBBUs的最小数量。仿真结果表明,与其他算法相比,NMBS有效地减少了C-RAN中的活动vBBUs数,降低了PC数。此外,该算法比分布式基线(DB)算法、首次拟合递减(FFD)算法和启发式模拟退火(HSA)算法分别减少了65%、6%和3%的BBU池平均PC。
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A parameterized and optimized BBU pool virtualization power model for C-RAN architecture
This paper introduces a cost effective scheme for a Cloud Radio Access Network (C-RAN) based on virtualization, which is a technology used to reduce Power Consumption (PC) in the Base Band Unit (BBU pool). The BBU's function is proposed to be as a software application running in a servers, which is called the virtual BBU (vBBU). To asses the proposal scheme, a power model of the BBU pool is proposed for C-RAN architecture. This provides a parametrized and optimized linear power model, which cover all the individual aspects in the C-RAN system relevant for the PC analysis in the BBU pool. Moreover, in this work a New Minimum Bin Slack (NMBS) algorithm is proposed, which combines with the Bin Packing Method (BPM) to minimize the number of vBBUs. The NMBS is aimed at matching the right amount of baseband processing load of Remote Radio Heads (RRHs) with load balancing as much as possible among all of the vBBUs, and switching off others. The baseband processing procedure for each vBBU is divided into N tasks, which can be processed by multiple vBBUs so as to offer system flexibility. The proposed architecture is based on integer linear programming for finding the minimum number of vBBUs. The simulation results show the NMBS effectively decreases the number of active vBBUs and decreases the PC in C-RAN when compared with other algorithms. In addition, this algorithm reduced the average PC in the BBU pool by 65%, 6%, and 3% less than for the Distributed Baseline (DB), the First Fit Decreasing (FFD) algorithm and the Heuristic Simulating Annealing (HSA) algorithm, respectively.
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