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Algorithms for Theoretical Investigation of Fairness in Multipath Transport

  • Amanpreet Singh
  • Andreas KönsgenEmail author
  • Hakim Adhari
  • Carmelita Görg
  • Erwin P. Rathgeb
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 158)

Abstract

With the onset of multipath transport protocols such as MPTCP and multihomed mobile devices, fairness considerations which have been widely analyzed for legacy TCP need to be re-investigated. A practical realization of fairness amongst different participants is known to be difficult but even the theoretical calculation of the resource capacity and its allocation is not a trivial task. Therefore in this work, resource allocation algorithms are presented to thoroughly evaluate the impact of the fairness definitions. For a rigorous analysis, existing fairness definitions are identified according to the resources (bottleneck or network) and the competing participants (flow, tariff or user). Tariff as the participant, provides a realistic option to comply with the service level agreement between the operator and the user where as flow as the participant leads to TCP-compatible allocation. From the obtained results, it can be seen that if fairness is applied at the bottleneck then it is absolutely fair to the individual participants w.r.t. the bottleneck. On the other hand, fairness mechanisms considering the whole network as a single resource exploit the freedom of resource allocation (due to multipath flows) to achieve an overall similar allocation for the different participants (irrespective if the participant is composed of singlepath or multipath flows) but are still restricted by the topological constraints and might even result in a lower overall network throughput (This work has been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft – DFG)).

Keywords

Source Node Congestion Control Fair Share Congestion Window Resource Allocation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015

Authors and Affiliations

  • Amanpreet Singh
    • 1
  • Andreas Könsgen
    • 1
    Email author
  • Hakim Adhari
    • 2
  • Carmelita Görg
    • 1
  • Erwin P. Rathgeb
    • 2
  1. 1.University of BremenBremenGermany
  2. 2.University of Duisburg-EssenEssenGermany

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