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Scalability-aware mechanism based on workload prediction in ultra-peer networks

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Abstract

Ultra-peers networks are emergent architectures in large-scale distributed computing environments. Controlling the workload is essential since the system scales up rapidly and accommodates a dynamic change in the number of users, resources, etc. Thus, developing an advanced model based on scalability aspects is a necessity, since it is an important and critical issue when designing such systems. In this paper, we present a scalability-aware approach for ultra-peers networks where each ultra-peer behaves like an ecosystem, in which, we prevent the presence of bottleneck in these structured information systems, and help each ultra-peer to find and stay in a steady state. We make use of neural networks in conjunction with the queueing theory to understand the behaviour of each ultra-peer, by first estimating its future workload, then to take decision on whether the next period will cause a bottleneck situation or not. After that we propose solutions to allow each ultra-peer to scale with the growth of the network size. The effectiveness of the design on scalability is evaluated using synthetic as well as realistic workloads for a number of different scenarios. Results show that the ultra-peer has successfully supervised its state to scale with the growth of the system size.

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Notes

  1. http://ita.ee.lbl.gov/html/contrib/ClarkNet-HTTP.html

  2. http://www.statgraphics.com/

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Authors and Affiliations

  1. Department of Computer Science and its applications, University of Abdelhamid Mehri Constantine 2, Ali Mendjeli, Constantine, Algeria

    Nabila Chergui & Salim Chikhi

  2. School of Informatics and Computer Science, University College Dublin, Dublin, Ireland

    Tahar Kechadi

Authors
  1. Nabila Chergui
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  2. Tahar Kechadi
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  3. Salim Chikhi
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Correspondence to Nabila Chergui.

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Chergui, N., Kechadi, T. & Chikhi, S. Scalability-aware mechanism based on workload prediction in ultra-peer networks. Peer-to-Peer Netw. Appl. 11, 431–449 (2018). https://doi.org/10.1007/s12083-017-0542-z

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