Energy-efficient fuzzy control model for GPU-accelerated packetrnclassification

摘要

As a core component of many network infrastructures, packet classification requires matching packetrnheaders against a series of predefined rules. Its performance determines, to some extent, how fast packetsrncan be processed. There already exists many proposals, which optimize the throughput of packetrnclassification, but few of them take power consumption into account. To meet the requirements of greenrnnetwork computing, this paper focuses on energy-efficient solutions that provide reasonable throughput asrnwell. Similar to recent advancements, the graphics processing unit (GPU) is adopted to accelerate rulernmatching. 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.rnAs demonstrated in the evaluation experiments, when the GPU is in the idle status, the proposed modelrncan save 10 W. In running status, the fuzzy control–based energy efficiency optimizing model can avoidrnGPU shutdown issue caused by GPU self-protection mechanism when the GPU temperature rises to 95°C.rnFurthermore, by improving the resource configuration of GPU kernels according to the model, the overallrnenergy efficiency is enhanced by up to 15.5%, while simultaneously keeping throughput at the same level.