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Model-based evaluation of the power versus performance of network routing algorithms

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Abstract

In order to optimize traffic flows and power consumption of network components, various green routing algorithms and protocols have been proposed. These algorithms and protocols apply different techniques to attain their own goals. One of the most important techniques is the sleep-scheduling technique that switches the status of the network components, nodes or links, into active/inactive modes. There are four characteristics affecting the power and performance of communication networks which distinguish green routing algorithms and protocols, namely the sleep-scheduled component, decision structure, network traffic awareness, and quality of service awareness. In this paper, a method is proposed to model, evaluate, and compare the power and performance of the green routing algorithms that use the sleep-scheduling technique. We apply stochastic activity networks to model and analyze the routing algorithms with respect to the network topology. The results obtained from the comparison of the algorithms, validated with the OMNeT++ simulator, can be used by network administrators to make the right decisions.

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Authors and Affiliations

  1. Trustworthy Computing Laboratory, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Ali Naghash Asadi & Mohammad Abdollahi Azgomi

  2. School of Computer Engineering, Iran University of Science and Technology, Hengam St., Resalat Sq., 16846-13114, Tehran, Iran

    Mohammad Abdollahi Azgomi

  3. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Reza Entezari-Maleki

Authors
  1. Ali Naghash Asadi
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  2. Mohammad Abdollahi Azgomi
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  3. Reza Entezari-Maleki
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Correspondence to Mohammad Abdollahi Azgomi.

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Asadi, A.N., Azgomi, M.A. & Entezari-Maleki, R. Model-based evaluation of the power versus performance of network routing algorithms. Computing 103, 1723–1746 (2021). https://doi.org/10.1007/s00607-020-00882-x

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