Deep reinforcement learning based resource allocation in low latency edge computing networks

Tianyu Yang, Yulin Hu, Mustafa C Gursoy, Anke Schmeink, Rudolf Mathar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

34 Scopus citations

Abstract

In this paper, we investigate strategies for the allocation of computational resources using deep reinforcement learning in mobile edge computing networks that operate with finite blocklength codes to support low latency communications. The end-to-end (E2E) reliability of the service is addressed, while both the delay violation probability and the decoding error probability are taken into account. By employing a deep reinforcement learning method, namely deep Q-learning, we design an intelligent agent at the edge computing node to develop a real-time adaptive policy for computational resource allocation for offloaded tasks of multiple users in order to improve the average E2E reliability. Via simulations, we show that under different task arrival rates, the realized policy serves to increase the task number that decreases the delay violation rate while guaranteeing an acceptable level of decoding error probability. Moreover, we show that the proposed deep reinforcement learning approach outperforms the random and equal scheduling benchmarks.

Original languageEnglish (US)
Title of host publication2018 15th International Symposium on Wireless Communication Systems, ISWCS 2018
PublisherVDE Verlag GmbH
Volume2018-August
ISBN (Electronic)9781538650059
DOIs
StatePublished - Oct 12 2018
Event15th International Symposium on Wireless Communication Systems, ISWCS 2018 - Lisbon, Portugal
Duration: Aug 28 2018Aug 31 2018

Other

Other15th International Symposium on Wireless Communication Systems, ISWCS 2018
CountryPortugal
CityLisbon
Period8/28/188/31/18

Keywords

  • Deep reinforcement learning
  • Edge computing
  • Finite blocklength coding
  • Ultra-reliable low-latency communications (urllc)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

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