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High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU

Jie Xu1, *, Qun Wang1, Yifan Wang1, Khan Asif2

1 Jiangsu Police Institute, Nanjing, 210031, China.
2 Department of Computer Application, Crescent Institutes of Science and Technology, Chennai, 600048, India.

* Corresponding Author: Jie Xu. Email: email.

Computers, Materials & Continua 2020, 64(3), 1819-1844.


Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors, such as the random parameters inherent in linear estimator and the random functions used to map a host to different linear estimators. These random factors cannot be reduced by merging multiple estimators, and existing algorithms cannot remove the deviation caused by such factors. In this paper, we regard the estimation deviation as a random variable and proposed a sampling method, recorded as the linear estimator array step sampling algorithm (L2S), to reduce the influence of the random deviation. L2S improves the accuracy of the estimated cardinalities by evaluating and remove the expected value of random deviation. The cardinality estimation algorithm based on the estimator array is a computationally intensive algorithm, which takes a lot of time when processing high-speed network data in a serial environment. To solve this problem, a method is proposed to port the cardinality estimating algorithm based on the estimator array to the Graphics Processing Unit (GPU). Experiments on real-world highspeed network traffic show that L2S can reduce the absolute bias by more than 22% on average, and the extra time is less than 61 milliseconds on average.


Cite This Article

J. Xu, Q. Wang, Y. Wang and K. Asif, "High accuracy network cardinalities estimation by step sampling revision on gpu," Computers, Materials & Continua, vol. 64, no.3, pp. 1819–1844, 2020.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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