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DOI: 10.4230/OASIcs.Fog-IoT.2020.6

Routing Using Safe Reinforcement Learning

Authors: Gautham Nayak Seetanadi and Karl-Erik Årzén

Published in: OASIcs, Volume 80, 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)

Abstract

The ever increasing number of connected devices has lead to a metoric rise in the amount data to be processed. This has caused computation to be moved to the edge of the cloud increasing the importance of efficiency in the whole of cloud. The use of this fog computing for time-critical control applications is on the rise and requires robust guarantees on transmission times of the packets in the network while reducing total transmission times of the various packets. We consider networks in which the transmission times that may vary due to mobility of devices, congestion and similar artifacts. We assume knowledge of the worst case tranmssion times over each link and evaluate the typical tranmssion times through exploration. We present the use of reinforcement learning to find optimal paths through the network while never violating preset deadlines. We show that with appropriate domain knowledge, using popular reinforcement learning techniques is a promising prospect even in time-critical applications.

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Gautham Nayak Seetanadi and Karl-Erik Årzén. Routing Using Safe Reinforcement Learning. In 2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020). Open Access Series in Informatics (OASIcs), Volume 80, pp. 6:1-6:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{nayakseetanadi_et_al:OASIcs.Fog-IoT.2020.6,
  author =	{Nayak Seetanadi, Gautham and \r{A}rz\'{e}n, Karl-Erik},
  title =	{{Routing Using Safe Reinforcement Learning}},
  booktitle =	{2nd Workshop on Fog Computing and the IoT (Fog-IoT 2020)},
  pages =	{6:1--6:8},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-144-3},
  ISSN =	{2190-6807},
  year =	{2020},
  volume =	{80},
  editor =	{Cervin, Anton and Yang, Yang},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.Fog-IoT.2020.6},
  URN =		{urn:nbn:de:0030-drops-120007},
  doi =		{10.4230/OASIcs.Fog-IoT.2020.6},
  annote =	{Keywords: Real time routing, safe exploration, safe reinforcement learning, time-critical systems, dynamic routing}
}

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DOI: 10.4230/LIPIcs.ECRTS.2018.17

Camera Networks Dimensioning and Scheduling with Quasi Worst-Case Transmission Time

Authors: Viktor Edpalm, Alexandre Martins, Karl-Erik Årzén, and Martina Maggio

Published in: LIPIcs, Volume 106, 30th Euromicro Conference on Real-Time Systems (ECRTS 2018)

Abstract

This paper describes a method to compute frame size estimates to be used in quasi Worst-Case Transmission Times (qWCTT) for cameras that transmit frames over IP-based communication networks. The precise determination of qWCTT allows us to model the network access scheduling problem as a multiframe problem and to re-use theoretical results for network scheduling. The paper presents a set of experiments, conducted in an industrial testbed, that validate the qWCTT estimation. We believe that a more precise estimation will lead to savings for network infrastructure and to better network utilization.

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Viktor Edpalm, Alexandre Martins, Karl-Erik Årzén, and Martina Maggio. Camera Networks Dimensioning and Scheduling with Quasi Worst-Case Transmission Time. In 30th Euromicro Conference on Real-Time Systems (ECRTS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 106, pp. 17:1-17:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{edpalm_et_al:LIPIcs.ECRTS.2018.17,
  author =	{Edpalm, Viktor and Martins, Alexandre and \r{A}rz\'{e}n, Karl-Erik and Maggio, Martina},
  title =	{{Camera Networks Dimensioning and Scheduling with Quasi Worst-Case Transmission Time}},
  booktitle =	{30th Euromicro Conference on Real-Time Systems (ECRTS 2018)},
  pages =	{17:1--17:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-075-0},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{106},
  editor =	{Altmeyer, Sebastian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2018.17},
  URN =		{urn:nbn:de:0030-drops-89869},
  doi =		{10.4230/LIPIcs.ECRTS.2018.17},
  annote =	{Keywords: worst-case transmission time, H.264, bandwidth estimation, video compression, network access scheduling, multiframe model, camera network}
}

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Documents authored by Årzén, Karl-Erik

Routing Using Safe Reinforcement Learning

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Camera Networks Dimensioning and Scheduling with Quasi Worst-Case Transmission Time

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