AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (1.4 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

MD-AVB: A Multi-Manifold Based Available Bandwidth Prediction Algorithm

Pei Zhang, Changqing An, Zhanfeng Wang( ), Fengyuan Ma
Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Institute for Network Sciences and Cyberspace, Tsinghua University, Beijing 100084, China.
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.
Show Author Information

Abstract

The performance of Internet applications is heavily affected by the end-to-end available bandwidth. Thus, it is very important to examine how to accurately predict the available Internet bandwidth. A number of available bandwidth prediction algorithms have been proposed to date, but none of the existing solutions are able to achieve a high level of accuracy. In this paper, a Multi-manifold based Available Bandwidth prediction algorithm (MD-AVB) is proposed, based on the observation that the available bandwidth space on the Internet is multi-manifold and asymmetrical. In the proposed algorithm, the available bandwidth space is divided into multiple lower-dimensional domains iteratively, and each domain is embedded separately to predict the available bandwidth. Experiments on HP S3 datasets demonstrate that the proposed algorithm is more accurate than existing approaches.

Keywords

available bandwidth spaceperformance predictionmulti-manifoldasymmetry

References

[1]
N. N. Hu and P. Steenkiste, Exploiting internet route sharing for large scale available bandwidth estimation, in Proc. 5th ACM SIGCOMM Conf. Internet Measurement, Berkeley, CA, USA, 2005, p. 16.
Crossref
[2]
A. Shriram, Efficient techniques for end-to-end bandwidth estimation: Performance evaluations and scalable deployment, https://cdr.lib.unc.edu/concern/dissertations/vm40xs86h, 2009.
[3]
C. Y. Xing, M. Chen, and L. Yang, Predicting available bandwidth of internet path with ultra metric space-based approaches, in Proc. GLOBECOM 2009—2009 IEEE Global Telecommunications Conf., Honolulu, HI, USA, 2009.
Crossref
[4]
P. Key, L. Massoulié, and D. C. Tomozei, Non-metric coordinates for predicting network proximity, in Proc. IEEE INFOCOM 2008—The 27th Conf. on Computer Communications, Phoenix, AZ, USA, 2008, pp. 1840-1848.
Crossref
[5]
T. S. E. Ng and H. Zhang, Predicting internet network distance with coordinates-based approaches, in Proc. 21st Ann. Joint Conf. of the IEEE Computer and Communications Societies, New York, NY, USA, 2002.
[6]
F. Dabek, R. Cox, F. Kaashoek, and R. Morris, Vivaldi: A decentralized network coordinate system, ACM SIGCOMM Comp. Commun. Rev., vol. 34, no. 4, pp. 15-26, 2004.
Crossref Google Scholar
[7]
V. Ramasubramanian, D. Malkhi, F. Kuhn, M. Balakrishnan, A. Gupta, and A. Akella, On the treeness of internet latency and bandwidth, in Proc. 11th Int. Joint Conf. on Measurement and Modeling of Computer Systems, Seattle, WA, USA, 2009.
Crossref
[8]
S. Song, P. J. Keleher, and A. Sussman, Searching for bandwidth-constrained clusters, in Proc. 31st Int. Conf. on Distributed Computing Systems, Minneapolis, MN, USA, 2011.
Crossref
[9]
C. Y. Xing, G. M. Zhang, B. Xu, C. Hu, and M. Chen, A hierarchical internet path available bandwidth inference mechanism, J. Int. Technol., vol. 18, no. 1, pp. 135-146, 2017.
Google Scholar
[10]
P. Yalagandula, P. Sharma, S. Banerjee, S. Basu, and S. Lee, S3: A scalable sensing service for monitoring large networked systems, in Proc. of 2006 SIGCOMM Workshop on Internet network Management, Pisa, Italy, 2006.
[11]
Y. Wang, Y. Jiang, Y. Wu, and Z. H. Zhou, Multi-manifold clustering, in Proc. 11th Pacific Rim Int. Conf. on Trends in Artificial Intelligence, Daegu, Korea, 2010.
Crossref
[12]
I. T. Jolliffe, Principal Component Analysis. Springer, 2011.
Crossref
[13]
J. B. Tenenbaum, V. De Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
Crossref Google Scholar
[14]
A. Ghodsi, Dimensionality reduction a short tutorial, https://www.math.uwaterloo.ca/~aghodsib/courses/f06stat890/readings/tutorial_stat890.pdf, 2006.
[15]
Z. F. Wang, M. Chen, C. Y. Xing, and G. M. Zhang, T-aware: An AS topology based available bandwidth prediction algorithm, (in Chinese), J. Beijing Univ. Posts Telecommun., vol. 34, no. 3, pp. 66-70, 2011.
Google Scholar
Tsinghua Science and Technology
Volume 25 Issue 1,
February 2020
Pages 140-148
DOI: 10.26599/TST.2019.9010002
Cite this article:
Zhang P, An C, Wang Z, et al. MD-AVB: A Multi-Manifold Based Available Bandwidth Prediction Algorithm. Tsinghua Science and Technology, 2020, 25(1): 140-148. https://doi.org/10.26599/TST.2019.9010002

638

Views

13

Downloads

2

Crossref

N/A

Web of Science

2

Scopus

1

CSCD

Altmetrics
Received: 27 November 2018
Revised: 18 February 2019
Accepted: 11 March 2019
Published: 22 July 2019
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return