The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami menganggap masalah pengenalpastian pantas aliran kadar tinggi dalam pautan tulang belakang dengan kemungkinan berjuta-juta aliran. Pengenalpastian tepat aliran kadar tinggi adalah penting untuk pengurusan baris gilir aktif, pengukuran trafik dan keselamatan rangkaian seperti pengesanan serangan penafian perkhidmatan yang diedarkan. Sukar untuk mengenal pasti secara langsung aliran kadar tinggi dalam pautan tulang belakang kerana menjejaki kemungkinan berjuta-juta aliran memerlukan ingatan kelajuan tinggi yang besar. Untuk mengurangkan overhed pengukuran, deterministik 1-out-of-k teknik persampelan diguna pakai yang juga dilaksanakan dalam penghala Cisco (NetFlow). Sebaik-baiknya, kaedah pengenalan aliran kadar tinggi harus mempunyai masa pengenalan yang singkat, kos memori yang rendah dan kos pemprosesan. Paling penting, ia harus dapat menentukan ketepatan pengenalan. Kami membangunkan dua kaedah sedemikian. Kaedah pertama adalah berdasarkan ujian saiz sampel tetap (FSST) yang mampu mengenal pasti aliran kadar tinggi dengan ketepatan pengenalan yang ditentukan pengguna. Walau bagaimanapun, memandangkan FSST perlu merekodkan setiap aliran sampel semasa tempoh pengukuran, ia tidak cekap memori. Oleh itu kaedah novel kedua berdasarkan ujian nisbah kebarangkalian jujukan terpotong (TSPRT) dicadangkan. Melalui persampelan berjujukan, TSPRT dapat mengeluarkan aliran kadar rendah dan mengenal pasti aliran kadar tinggi pada peringkat awal yang boleh mengurangkan kos ingatan dan masa pengenalan masing-masing. Mengikut cara untuk menentukan parameter dalam TSPRT, dua versi TSPRT dicadangkan: TSPRT-M yang sesuai apabila kos ingatan rendah diutamakan dan TSPRT-T yang sesuai apabila masa pengenalan yang singkat diutamakan. Keputusan eksperimen menunjukkan bahawa TSPRT memerlukan kurang memori dan masa pengenalan dalam mengenal pasti aliran kadar tinggi sambil memenuhi keperluan ketepatan berbanding kaedah yang dicadangkan sebelum ini.
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Salinan
Yu ZHANG, Binxing FANG, Hao LUO, "Identifying High-Rate Flows Based on Sequential Sampling" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 5, pp. 1162-1174, May 2010, doi: 10.1587/transinf.E93.D.1162.
Abstract: We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1162/_p
Salinan
@ARTICLE{e93-d_5_1162,
author={Yu ZHANG, Binxing FANG, Hao LUO, },
journal={IEICE TRANSACTIONS on Information},
title={Identifying High-Rate Flows Based on Sequential Sampling},
year={2010},
volume={E93-D},
number={5},
pages={1162-1174},
abstract={We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.},
keywords={},
doi={10.1587/transinf.E93.D.1162},
ISSN={1745-1361},
month={May},}
Salinan
TY - JOUR
TI - Identifying High-Rate Flows Based on Sequential Sampling
T2 - IEICE TRANSACTIONS on Information
SP - 1162
EP - 1174
AU - Yu ZHANG
AU - Binxing FANG
AU - Hao LUO
PY - 2010
DO - 10.1587/transinf.E93.D.1162
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2010
AB - We consider the problem of fast identification of high-rate flows in backbone links with possibly millions of flows. Accurate identification of high-rate flows is important for active queue management, traffic measurement and network security such as detection of distributed denial of service attacks. It is difficult to directly identify high-rate flows in backbone links because tracking the possible millions of flows needs correspondingly large high speed memories. To reduce the measurement overhead, the deterministic 1-out-of-k sampling technique is adopted which is also implemented in Cisco routers (NetFlow). Ideally, a high-rate flow identification method should have short identification time, low memory cost and processing cost. Most importantly, it should be able to specify the identification accuracy. We develop two such methods. The first method is based on fixed sample size test (FSST) which is able to identify high-rate flows with user-specified identification accuracy. However, since FSST has to record every sampled flow during the measurement period, it is not memory efficient. Therefore the second novel method based on truncated sequential probability ratio test (TSPRT) is proposed. Through sequential sampling, TSPRT is able to remove the low-rate flows and identify the high-rate flows at the early stage which can reduce the memory cost and identification time respectively. According to the way to determine the parameters in TSPRT, two versions of TSPRT are proposed: TSPRT-M which is suitable when low memory cost is preferred and TSPRT-T which is suitable when short identification time is preferred. The experimental results show that TSPRT requires less memory and identification time in identifying high-rate flows while satisfying the accuracy requirement as compared to previously proposed methods.
ER -