SPArch: A Hardware-oriented Sketch-based Architecture for High-speed Network Flow Measurements

Author:

Sateesan Arish1ORCID,Vliegen Jo2ORCID,Scherrer Simon3ORCID,Hsiao Hsu-Chun45ORCID,Perrig Adrian6ORCID,Mentens Nele27ORCID

Affiliation:

1. ES&S-COSIC, ESAT, KU Leuven, Leuven, Belgium

2. ES&S-COSIC, ESAT, KU Leuven, Leuven Belgium

3. Department of Computer Science, ETH Zurich, Zurich Switzerland

4. National Taiwan University, Taipei Taiwan

5. Academia Sinica, Taipei Taiwan

6. Department of Computer Science, ETH Zurich, Zurich, Switzerland

7. LIACS, Leiden University, Leiden, Netherlands

Abstract

Network flow measurement is an integral part of modern high-speed applications for network security and data-stream processing. However, processing at line rate while maintaining the required data structure within the on-chip memory of the hardware platform is a challenging task for measurement algorithms, especially when accuracy is of primary importance, such as in network security applications. Most of the existing measurement algorithms are no exception to such issues when deployed in high-speed networking environments and are also not tailored for efficient hardware implementation. Sketch-based measurement algorithms minimize the memory requirement and are suitable for high-speed networks but possess a low memory-accuracy trade-off and lack the versatility of individual flow mapping. To address these challenges, we present a hardware-friendly data structure named Sketch-based Pseudo-associative array Architecture (SPArch). SPArch is highly accurate and extremely memory-efficient, making it suitable for network flow measurement and security applications. The parallelism in SPArch ensures minimal and constant memory access cycles. Unlike other sketch architectures, SPArch provides the functionality of individual flow mapping similar to associative arrays, and the optimized version of SPArch allows the organization of counters in multiple buckets based on the flow sizes. An in-depth analysis of SPArch is carried out in this paper and implemented SPArch on the Alveo data center accelerator card, demonstrating its suitability for high-speed networks.

Publisher

Association for Computing Machinery (ACM)

Reference45 articles.

1. Network traffic characteristics of data centers in the wild

2. The Center for Applied Internet Data Analysis CAIDA. 2018. Passive OC48 and OC192 Traces. https://www.caida.org/data/passive/trace_stats/nyc-B/2018/?monitor=20181018-130000.UTC. Accessed: 2023.

3. Moses Charikar, Kevin Chen, and Martin Farach-Colton. 2002. Finding Frequent Items in Data Streams. In Automata, Languages and Programming. Springer Berlin Heidelberg, Berlin, Heidelberg, 693–703.

4. Counter Tree: A Scalable Counter Architecture for Per-Flow Traffic Measurement

5. Serving DNNs in Real Time at Datacenter Scale with Project Brainwave

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3