Efficient algorithms for collecting the statistics of large-scale IP address data.

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Bibliographic Details
Title:	Efficient algorithms for collecting the statistics of large-scale IP address data.
Authors:	Hui Liu¹ lxyliuhui@163.com, Yi Cao¹ cycaoyi@hotmail.com, Zehan Cai¹ lxyliuhui@hotmail.com, Hua Mao² hua.mao@northumbria.ac.uk, Jie Chen³ chenjie2010@scu.edu.cn
Source:	Computer Science & Information Systems. Jun2025, Vol. 22 Issue 3, p927-944. 18p.
Subjects:	Computer network traffic, Internet protocol address, Time complexity, Traffic flow measurement, Dynamic balance (Mechanics)
Abstract:	Compiling the statistics of large-scale IP address data is an essential task in network traffic measurement. The statistical results are used to evaluate the potential impact of user behaviors on network traffic. This requires algorithms that are capable of storing and retrieving a high volume of IP addresses within time and memory constraints. In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP addresses that balance time efficiency and memory consumption. The proposed solutions take into account the sparse nature of the statistics of IP addresses while maintaining a dynamic balance among layered memory blocks. There are two layers in the first proposed method, each of which contains a limited number of memory blocks. Each memory block contains 256 elements of size 256×8 bytes for a 64-bit system. In contrast to built-in hash mapping functions, the proposed solution completely avoids expensive hash collisions while retaining the linear time complexity of hash-based solutions. Moreover, the mechanism dynamically determines the hash index length according to the range of IP addresses, and can balance the time and memory constraints. In addition, we propose an efficient parallel scheme to speed up the collection of statistics. The experimental results on several synthetic datasets show that the proposed method substantially outperforms the baselines with respect to time and memory space efficiency. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Compiling the statistics of large-scale IP address data is an essential task in network traffic measurement. The statistical results are used to evaluate the potential impact of user behaviors on network traffic. This requires algorithms that are capable of storing and retrieving a high volume of IP addresses within time and memory constraints. In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP addresses that balance time efficiency and memory consumption. The proposed solutions take into account the sparse nature of the statistics of IP addresses while maintaining a dynamic balance among layered memory blocks. There are two layers in the first proposed method, each of which contains a limited number of memory blocks. Each memory block contains 256 elements of size 256×8 bytes for a 64-bit system. In contrast to built-in hash mapping functions, the proposed solution completely avoids expensive hash collisions while retaining the linear time complexity of hash-based solutions. Moreover, the mechanism dynamically determines the hash index length according to the range of IP addresses, and can balance the time and memory constraints. In addition, we propose an efficient parallel scheme to speed up the collection of statistics. The experimental results on several synthetic datasets show that the proposed method substantially outperforms the baselines with respect to time and memory space efficiency. [ABSTRACT FROM AUTHOR]
ISSN:	18200214
DOI:	10.2298/CSIS241201033L