Energy‐efficient fuzzy control model for GPU‐accelerated packet classification. (8th February 2017)

• Record Type: Journal Article
• Authors: Li, Guo; Zhang, Dafang; Li, Yanbiao; Zheng, Jintao; Li, Keqin
• Other Names: Lengauer Christian guestEditor.; Bolten Matthias guestEditor.; Falgout Robert guestEditor.; Schenk Olaf guestEditor.; Zhou Xiaobo guestEditor.; Zhao Laiping guestEditor.
• Is Part Of: Concurrency and computation. Volume 29:Number 17(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-08
• Subjects: energy‐efficient -- fuzzy control -- GPU -- packet classification; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4079
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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°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.