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NEAT-TCP: Generation of TCP Congestion Control Through Neuroevolution of Augmenting Topologies

Wallaschek, Kay Luis and Klose, Robin and Almon, Lars and Hollick, Matthias (2020):
NEAT-TCP: Generation of TCP Congestion Control Through Neuroevolution of Augmenting Topologies.
In: IEEE International Conference on Communications (icc2020), virtual Conference, 07.-11. June, pp. 1-6, [Online-Edition: https://icc2020.ieee-icc.org],
[Conference or Workshop Item]

Abstract

We present NEAT-TCP, a novel technique to automatically generate congestion control algorithms in a data-driven fashion while optimizing towards a specified global system utility. NEAT-TCP employs an artificial neural network (ANN) in each node and generates a population of ANNs by means of an evolutionary algorithm called NEAT. The ANNs run independently from each other at the communication endpoints and take only features as inputs that are locally available at these nodes. We define the system utility as a combined maximization of overall throughput and throughput fairness between flows according to Jain's fairness index. The nodes are deployed in a grid topology in ns-3 simulations, which makes it particularly difficult to maximize the utility due to different interference levels for the data flows. In our experiments, NEAT-TCP achieves 69 % more fairness, 66 % less mean end-to-end delay and 71 % less packet loss in relation to TCP New Reno at the cost of 19 % less overall throughput, which meets our multi-criteria objective.

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Wallaschek, Kay Luis and Klose, Robin and Almon, Lars and Hollick, Matthias
Title: NEAT-TCP: Generation of TCP Congestion Control Through Neuroevolution of Augmenting Topologies
Language: English
Abstract:

We present NEAT-TCP, a novel technique to automatically generate congestion control algorithms in a data-driven fashion while optimizing towards a specified global system utility. NEAT-TCP employs an artificial neural network (ANN) in each node and generates a population of ANNs by means of an evolutionary algorithm called NEAT. The ANNs run independently from each other at the communication endpoints and take only features as inputs that are locally available at these nodes. We define the system utility as a combined maximization of overall throughput and throughput fairness between flows according to Jain's fairness index. The nodes are deployed in a grid topology in ns-3 simulations, which makes it particularly difficult to maximize the utility due to different interference levels for the data flows. In our experiments, NEAT-TCP achieves 69 % more fairness, 66 % less mean end-to-end delay and 71 % less packet loss in relation to TCP New Reno at the cost of 19 % less overall throughput, which meets our multi-criteria objective.

Divisions: 20 Department of Computer Science
20 Department of Computer Science > Sichere Mobile Netze
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1053: MAKI – Multi-Mechanisms Adaptation for the Future Internet > C: Communication Mechanisms > Subproject C1: Network-centred perspective
Event Title: IEEE International Conference on Communications (icc2020)
Event Location: virtual Conference
Event Dates: 07.-11. June
Date Deposited: 22 Apr 2020 11:27
Official URL: https://icc2020.ieee-icc.org
Additional Information:

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