A novel LMS method for real-time network traffic prediction

Yang Xinyu; Zeng Ming; Zhao Rui; Shi Yi

doi:10.1007/978-3-540-24768-5_14

A novel LMS method for real-time network traffic prediction

Yang Xinyu
, Zeng Ming
, Zhao Rui
, Shi Yi

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

Real-time traffic prediction could give important information to both network efficiency and QoS guarantees. On the basis of LMS algorithm, this paper presents an improved LMS predictor - EaLMS (Error-adjusted LMS) -for fundamental traffic prediction. The main idea of EaLMS is using previous prediction errors to adjust the LMS prediction value, so that the prediction delay could be decreased. The prediction experiment based on real traffic trace has proved that for short-term traffic prediction, compared with traditional LMS predictor, EaLMS significantly reduces prediction delay, especially at traffic burst moments, and avoids the problem of augmenting prediction error at the same time.

Original language	English
Title of host publication	Computational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings
Publisher	Springer Verlag
Pages	127-136
Number of pages	10
Edition	PART 4
ISBN (Print)	3540220607, 9783540220602
DOIs	https://doi.org/10.1007/978-3-540-24768-5_14
State	Published - 2004
Event	International Conference on Computational Science and Its Applications, ICCSA 2004 - Assisi, Italy Duration: 14 May 2004 → 17 May 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 4
Volume	3046 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Computational Science and Its Applications, ICCSA 2004
Country/Territory	Italy
City	Assisi
Period	14/05/04 → 17/05/04

Access to Document

10.1007/978-3-540-24768-5_14

Cite this

Xinyu, Y., Ming, Z., Rui, Z., & Yi, S. (2004). A novel LMS method for real-time network traffic prediction. In Computational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings (PART 4 ed., pp. 127-136). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3046 LNCS, No. PART 4). Springer Verlag. https://doi.org/10.1007/978-3-540-24768-5_14

Xinyu, Yang ; Ming, Zeng ; Rui, Zhao et al. / A novel LMS method for real-time network traffic prediction. Computational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings. PART 4. ed. Springer Verlag, 2004. pp. 127-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4).

@inproceedings{e4934cc968af4522a30c54cd00657a61,

title = "A novel LMS method for real-time network traffic prediction",

abstract = "Real-time traffic prediction could give important information to both network efficiency and QoS guarantees. On the basis of LMS algorithm, this paper presents an improved LMS predictor - EaLMS (Error-adjusted LMS) -for fundamental traffic prediction. The main idea of EaLMS is using previous prediction errors to adjust the LMS prediction value, so that the prediction delay could be decreased. The prediction experiment based on real traffic trace has proved that for short-term traffic prediction, compared with traditional LMS predictor, EaLMS significantly reduces prediction delay, especially at traffic burst moments, and avoids the problem of augmenting prediction error at the same time.",

author = "Yang Xinyu and Zeng Ming and Zhao Rui and Shi Yi",

year = "2004",

doi = "10.1007/978-3-540-24768-5\_14",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

number = "PART 4",

pages = "127--136",

booktitle = "Computational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings",

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note = "International Conference on Computational Science and Its Applications, ICCSA 2004 ; Conference date: 14-05-2004 Through 17-05-2004",

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Xinyu, Y, Ming, Z, Rui, Z & Yi, S 2004, A novel LMS method for real-time network traffic prediction. in Computational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings. PART 4 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 4, vol. 3046 LNCS, Springer Verlag, pp. 127-136, International Conference on Computational Science and Its Applications, ICCSA 2004, Assisi, Italy, 14/05/04. https://doi.org/10.1007/978-3-540-24768-5_14

A novel LMS method for real-time network traffic prediction. / Xinyu, Yang; Ming, Zeng; Rui, Zhao et al.
Computational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings. PART 4. ed. Springer Verlag, 2004. p. 127-136 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3046 LNCS, No. PART 4).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - Real-time traffic prediction could give important information to both network efficiency and QoS guarantees. On the basis of LMS algorithm, this paper presents an improved LMS predictor - EaLMS (Error-adjusted LMS) -for fundamental traffic prediction. The main idea of EaLMS is using previous prediction errors to adjust the LMS prediction value, so that the prediction delay could be decreased. The prediction experiment based on real traffic trace has proved that for short-term traffic prediction, compared with traditional LMS predictor, EaLMS significantly reduces prediction delay, especially at traffic burst moments, and avoids the problem of augmenting prediction error at the same time.

AB - Real-time traffic prediction could give important information to both network efficiency and QoS guarantees. On the basis of LMS algorithm, this paper presents an improved LMS predictor - EaLMS (Error-adjusted LMS) -for fundamental traffic prediction. The main idea of EaLMS is using previous prediction errors to adjust the LMS prediction value, so that the prediction delay could be decreased. The prediction experiment based on real traffic trace has proved that for short-term traffic prediction, compared with traditional LMS predictor, EaLMS significantly reduces prediction delay, especially at traffic burst moments, and avoids the problem of augmenting prediction error at the same time.

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Xinyu Y, Ming Z, Rui Z, Yi S. A novel LMS method for real-time network traffic prediction. In Computational Science and Its Applications - ICCSA 2004 - International Conference, Proceedings. PART 4 ed. Springer Verlag. 2004. p. 127-136. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 4). doi: 10.1007/978-3-540-24768-5_14

A novel LMS method for real-time network traffic prediction

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