Efficient algorithms for collecting the statistics of large-scale IP address data.
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| Title: | Efficient algorithms for collecting the statistics of large-scale IP address data. |
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| 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] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 186513823 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Efficient algorithms for collecting the statistics of large-scale IP address data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hui+Liu%22">Hui Liu</searchLink><relatesTo>1</relatesTo><i> lxyliuhui@163.com</i><br /><searchLink fieldCode="AR" term="%22Yi+Cao%22">Yi Cao</searchLink><relatesTo>1</relatesTo><i> cycaoyi@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Zehan+Cai%22">Zehan Cai</searchLink><relatesTo>1</relatesTo><i> lxyliuhui@hotmail.com</i><br /><searchLink fieldCode="AR" term="%22Hua+Mao%22">Hua Mao</searchLink><relatesTo>2</relatesTo><i> hua.mao@northumbria.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Jie+Chen%22">Jie Chen</searchLink><relatesTo>3</relatesTo><i> chenjie2010@scu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Jun2025, Vol. 22 Issue 3, p927-944. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+network+traffic%22">Computer network traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+protocol+address%22">Internet protocol address</searchLink><br /><searchLink fieldCode="DE" term="%22Time+complexity%22">Time complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+flow+measurement%22">Traffic flow measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamic+balance+%28Mechanics%29%22">Dynamic balance (Mechanics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS241201033L Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 927 Subjects: – SubjectFull: Computer network traffic Type: general – SubjectFull: Internet protocol address Type: general – SubjectFull: Time complexity Type: general – SubjectFull: Traffic flow measurement Type: general – SubjectFull: Dynamic balance (Mechanics) Type: general Titles: – TitleFull: Efficient algorithms for collecting the statistics of large-scale IP address data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hui Liu – PersonEntity: Name: NameFull: Yi Cao – PersonEntity: Name: NameFull: Zehan Cai – PersonEntity: Name: NameFull: Hua Mao – PersonEntity: Name: NameFull: Jie Chen IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 22 – Type: issue Value: 3 Titles: – TitleFull: Computer Science & Information Systems Type: main |
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