A prediction-based model for consistent adaptive routing in back-bone networks at extreme situations

Zhou, Q. and Pezaros, D. (2020) A prediction-based model for consistent adaptive routing in back-bone networks at extreme situations. Electronics, 9(12), 2146. (doi: 10.3390/electronics9122146)

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To reduce congestion, numerous routing solutions have been proposed for backbone networks, but how to select paths that stay consistently optimal for a long time in extremely congested situations, avoiding the unnecessary path reroutings, has not yet been investigated much. To solve that issue, a model that can measure the consistency of path latency difference is needed. In this paper, we make a humble step towards a consistent differential path latency model and by predicting base on that model, a metric Path Swap Indicator (PSI) is proposed. By learning the history latency of all optional paths, PSI is able to predict the onset of an obvious and steady channel deterioration and make the decision to switch paths. The effect of PSI is evaluated from the following aspects: (1) the consistency of the path selected, by measuring the time interval between PSI changes; (2) the accuracy of the channel congestion situation prediction; and (3) the improvement of the congestion situation. Experiments were carried out on a testbed using real-life Abilene traffic datasets collected at different times and locations. Results show that the proposed PSI can stay consistent for over 1000 s on average, and more than 3000 s at the longest in our experiment, while at the same time achieving a congestion situation improvement of more than 300% on average, and more than 200% at the least. It is evident that the proposed PSI metric is able to provide a consistent channel congestion prediction with satisfiable channel improvement at the same time. The results also demonstrate how different parameter values impact the result, both in terms of prediction consistency and the congestion improvement.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Zhou, Miss Qianru and Pezaros, Professor Dimitrios
Creator Roles:
Pezaros, D.Conceptualization, Investigation, Resources, Data curation, Writing – review and editing, Supervision, Project administration, Funding acquisition
Zhou, Q.Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization
Authors: Zhou, Q., and Pezaros, D.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Electronics
ISSN (Online):2079-9292
Published Online:15 December 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Electronics 9(12): 2146
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172888Network Measurement as a Service (MaaS)Dimitrios PezarosEngineering and Physical Sciences Research Council (EPSRC)EP/N033957/1Computing Science
173446FRuIT: The Federated RaspberryPi Micro-Infrastructure TestbedJeremy SingerEngineering and Physical Sciences Research Council (EPSRC)EP/P004024/1Computing Science
300952HIRP 2017 - Distributed Intelligence for Network ControlDimitrios PezarosHuawei Technologies (CN) (HUAWE-CN)N/AComputing Science