HALO: Accelerating flow classification for scalable packet processing in NFV

Yifan Yuan, Yipeng Wang, Ren Wang, Jian Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Network Function Virtualization (NFV) has become the new standard in the cloud platform, as it provides the flexibility and agility for deploying various network services on general-purpose servers. However, it still suffers from sub-optimal performance in software packet processing. Our characterization study of virtual switches shows that the flow classification is the major bottleneck that limits the throughput of the packet processing in NFV, even though a large portion of the classification rules can be cached in the last level cache (LLC) in modern servers. To overcome this bottleneck, we propose Halo, an effective near-cache computing solution for accelerating the flow classification. Halo exploits the hardware parallelism of the cache architecture consists of Non-Uniform Cache Access (NUCA) and Caching and Home Agent (CHA) available in almost all Intel multi-core CPUs. It associates the accelerator with each CHA component to speed up and scale the flow classification within LLC. To make Halo more generic, we extend the x86 - 64 instruction set with three simple data lookup instructions for utilizing the proposed near-cache accelerators. We develop Halo with the full-system simulator gem5. The experiments with a variety of real-world workloads of network services demonstrate that Halo improves the throughput of basic flow-rule lookup operations by 3.3×, and scales the representative flow classification algorithm - tuple space search by up to 23.4× with negligible negative impact on the performance of collocated network services, compared with state-of-the-art software-based solutions. Halo also performs up to 48.2× more energy-efficient than the fastest but expensive ternary content-addressable memory (TCAM), with trivial power and area overhead.

Original languageEnglish (US)
Title of host publicationISCA 2019 - Proceedings of the 2019 46th International Symposium on Computer Architecture
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages601-614
Number of pages14
ISBN (Electronic)9781450366694
DOIs
StatePublished - Jun 22 2019
Event46th International Symposium on Computer Architecture, ISCA 2019 - Phoenix, United States
Duration: Jun 22 2019Jun 26 2019

Publication series

NameProceedings - International Symposium on Computer Architecture
ISSN (Print)1063-6897

Conference

Conference46th International Symposium on Computer Architecture, ISCA 2019
CountryUnited States
CityPhoenix
Period6/22/196/26/19

Keywords

  • Flow classification
  • Hash-table lookup
  • Near-cache computing
  • Network function virtualization

ASJC Scopus subject areas

  • Hardware and Architecture

Fingerprint Dive into the research topics of 'HALO: Accelerating flow classification for scalable packet processing in NFV'. Together they form a unique fingerprint.

  • Cite this

    Yuan, Y., Wang, Y., Wang, R., & Huang, J. (2019). HALO: Accelerating flow classification for scalable packet processing in NFV. In ISCA 2019 - Proceedings of the 2019 46th International Symposium on Computer Architecture (pp. 601-614). (Proceedings - International Symposium on Computer Architecture). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3307650.3322272