Earticle

초록

영어

In the high-speed backbone network, with the increasing speed of network link, the number of network flows increase rapidly. Meanwhile, with restrictions on hardware computing and storage resources, so, how to identify and measure large flows timely and accurately in massive data become a hot issue in high speed network flow measurement area. In this paper, we propose a new algorithm based on double hash algorithm to realize large flow frequent items identification, according to the defect of MF algorithm which produces false positive easily and frequent updates to bring the huge pressure to the system. The complexity and false positive rate of the algorithm was analyzed. The effect of large flow frequent items statistical accuracy and discard rate for parameter configuration was analyzed through simulation. The theoretical analysis and the simulation result indicate that compare to MF algorithm, our algorithm can identify large flow frequent items more accurately, and satisfies the need of actual measurement.

목차

Abstract
 1. Introduction
 2. Definition of Network Flow and Frequent Items
 3. Multistage Filters Algorithm
 4. Double Hash Algorithm
  4.1. Time Bloom Filter
  4.2. Counting Bloom Filter
  4.3. Double Hash Algorithm
  4.4. Performance Evaluation
 5. Experimental Analysis
 References

키워드

저자

• Lei Bai [ Computer Department, North China Institute of Science and Technology ]
• Chao Chen [ Computer Department, North China Institute of Science and Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605