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RRPS: A Ranked Real-Time Publish/Subscribe Using Adaptive QoS

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Computational Science and Its Applications – ICCSA 2009 (ICCSA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5593))

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Abstract

Publish-Subscribe paradigm has been widely employed in Real-Time applications. However, the existing technologies and models only support a simple binary concept of matching: an event either matches a subscription or it does not; for instance, a production monitoring event will either match or not match a subscription for production anomaly. Based on adaptive Quality of Service (QoS) management, we propose a novel publish/subscribe model, which is implemented as a critical service in a real-time database Agilor. We argue that publications have different relevance to a subscription. On the premise of guaranteeing deadline d, a subscriber approximately receives k most relevant publications, where k and d are parameters defined by each subscription. After the architecture of our model is described, we present negotiations between components and scalable strategies for adaptive QoS management. Then, we propose an efficient algorithm to select different strategies adaptively depending on estimation of current QoS. Furthermore, we experimentally evaluate our model on real production data collected from manufacture industry to demonstrate its applicability in practice.

This work was supported in part by the National High Technology Research and Development Program (”863”Program) of China under Grant No. 2007AA040702.

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Author information

Authors and Affiliations

  1. Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Xinjie Lu, Tian Yang, Zaifei Liao, Wei Liu & Hongan Wang

  2. State Key Lab. of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Hongan Wang

  3. Department of Computer Science and Technology, Shandong University, Jinan Shandong, 250101, China

    Xin Li

  4. Graduate University of the Chinese Academy of Sciences, Beijing, 100049, China

    Xinjie Lu, Tian Yang & Zaifei Liao

Authors
  1. Xinjie Lu
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  2. Xin Li
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  3. Tian Yang
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  4. Zaifei Liao
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  5. Wei Liu
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  6. Hongan Wang
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

  2. School of Business Systems, Clayton, Monash University, 3800, Victoria, Australia

    David Taniar

  3. L.I.S.U.T. - D.A.P.I.T., University of Basilicata, Viale dell’Ateneo Lucano 10, 85100, Potenza, Italy

    Beniamino Murgante

  4. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, 06123, Perugia, Italy

    Antonio Laganà

  5. School of Computing, Computer Communication Laboratory, SoongSil University, 1-1 Sang-do 5 Dong, Dong-jak Ku, 156-743, Seoul, Korea

    Youngsong Mun

  6. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

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© 2009 Springer-Verlag Berlin Heidelberg

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Lu, X., Li, X., Yang, T., Liao, Z., Liu, W., Wang, H. (2009). RRPS: A Ranked Real-Time Publish/Subscribe Using Adaptive QoS. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02457-3_68

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  • DOI: https://doi.org/10.1007/978-3-642-02457-3_68

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02456-6

  • Online ISBN: 978-3-642-02457-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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