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A Scalable Network Proximity Estimate Algorithm for the Service Provider Selection Method

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Human Centered Computing (HCC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8944))

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Abstract

With the increasing development of overlay service network which contributes a significant portion of today’s network traffic, the selection of good service providers becomes more and more essential. In the selection, network distance is a very important parameter, and estimating network proximity is part of the network distance estimation. Although there exists a number of network proximity estimation technologies, they either require the distance measurement to all the potential targets, or fail when some landmark nodes are not available at a given instant of time. In this paper, we propose a network proximity technique that uses information obtained from probing a small number of landmarks. We firstly partite all the notes into different clusters based on their level vector such that nodes that fall within the same given cluster are relatively closer than those in the different clusters in terms of network latency. Then, for each cluster, the vector distance between the client and each service provider is combined with their ISP information to determine the K closest ones for the selection of the good service providers to consult. Our network proximity estimation strategy is simple, scalable, distributed with little support from the measurement infrastructures and no direct communications between the client and the service providers, and most importantly, it works well when some landmarks are invalid. Our strategy is tested using simulation. Our results indicate that the performance of network distance estimation in the service provider selection can be significantly improved by our scheme with limited measurements.

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Authors and Affiliations

  1. School of Computing, Beijing University of Posts and Telecommunications, Beijing, China

    Ting Wang, Ke Xu & Meina Song

Authors
  1. Ting Wang
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  2. Ke Xu
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  3. Meina Song
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Corresponding author

Correspondence to Ting Wang .

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Editors and Affiliations

  1. Wuhan University of Technology, Wuhan, China

    Qiaohong Zu

  2. Fujitsu Laboratories of Europe, Middlesex, United Kingdom

    Bo Hu

  3. Fudan University, Shanghai, China

    Ning Gu

  4. Telecommunications and Information Communication Technology, National Institute of Posts, Phnom Penh, Cambodia

    Sopheap Seng

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Wang, T., Xu, K., Song, M. (2015). A Scalable Network Proximity Estimate Algorithm for the Service Provider Selection Method. In: Zu, Q., Hu, B., Gu, N., Seng, S. (eds) Human Centered Computing. HCC 2014. Lecture Notes in Computer Science(), vol 8944. Springer, Cham. https://doi.org/10.1007/978-3-319-15554-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-15554-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15553-1

  • Online ISBN: 978-3-319-15554-8

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