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Predicting expected TCP throughput using genetic algorithm
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science.
Univ Politecn Cataluna, C Jordi Girona 1-3, Barcelona, Spain..
2016 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 108, 307-322 p.Article in journal (Refereed) Published
Abstract [en]

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence. 

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 108, 307-322 p.
Keyword [en]
Genetic algorithm, TCP throughput, Prediction, IEEE 802.11
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-47581DOI: 10.1016/j.comnet.2016.08.027ISI: 000385595600023OAI: oai:DiVA.org:kau-47581DiVA: diva2:1062159
Available from: 2017-01-04 Created: 2017-01-04 Last updated: 2017-01-04Bibliographically approved

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Benet, Cristian HernandezKassler, Andreas
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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