Energy-efficient fuzzy control model for GPU-accelerated packet classification
Energy-efficient fuzzy control model for GPU-accelerated packet classification
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DOI：
10.1002/cpe.4079
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发表时间：
2017-09-10
影响因子：
2
通讯作者：
Li, Keqin
中科院分区：
文献类型：
--
作者：
Li, Guo;Zhang, Dafang;Li, Keqin
作为许多网络基础设施的核心组件，数据包分类需要将数据包报头与一系列预定义的规则进行匹配。它的性能在某种程度上决定了数据包的处理速度。目前已有许多优化分组分类吞吐量的建议，但很少考虑功耗。为了满足绿色网络计算的要求，本文重点研究了节能的解决方案，提供合理的吞吐量。与最近的进展类似，采用图形处理单元（GPU）来加速规则匹配。然后，受空调变频能耗模型的启发，提出了一种基于模糊控制的GPU加速数据包分类能效优化模型。评估实验表明，当GPU处于空闲状态时，该模型可以节省10 W的功耗。在运行状态下，基于模糊控制的能效优化模型可以避免GPU温度上升到95 ℃时，GPU自保护机制导致的GPU关机问题。此外，通过根据该模型改进GPU内核的资源配置，整体能源效率提高了15.5%，同时保持吞吐量在同一水平。
As a core component of many network infrastructures, packet classification requires matching packet headers against a series of predefined rules. Its performance determines, to some extent, how fast packets can be processed. There already exists many proposals, which optimize the throughput of packet classification, but few of them take power consumption into account. To meet the requirements of green network computing, this paper focuses on energy-efficient solutions that provide reasonable throughput as well. Similar to recent advancements, the graphics processing unit (GPU) is adopted to accelerate rule matching. Then, inspired by the frequency-variable energy-consuming model for air conditioners, a fuzzy control-based energy efficiency optimizing model is proposed for GPU-accelerated packet classification. As demonstrated in the evaluation experiments, when the GPU is in the idle status, the proposed model can save 10 W. In running status, the fuzzy control-based energy efficiency optimizing model can avoid GPU shutdown issue caused by GPU self-protection mechanism when the GPU temperature rises to 95 degrees C. Furthermore, by improving the resource configuration of GPU kernels according to the model, the overall energy efficiency is enhanced by up to 15.5%, while simultaneously keeping throughput at the same level.