Abstract
Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q networks (DDQN) based learning scheme for task assignment in dynamic MEC networks, which enables multiple distributed edge nodes and a cloud data center to jointly process user tasks to achieve optimal long-term quality of service (QoS). The proposed scheme captures a wide range of dynamic network parameters including non-stationary node computing capabilities, network delay statistics, and task arrivals. It learns the optimal task assignment policy with no assumption on the knowledge of the underlying dynamics. In addition, the proposed algorithm accounts for both performance and complexity, and addresses the state and action space explosion problem in conventional Q learning. The evaluation results show that the proposed DDQN-based task assignment scheme significantly improves the QoS performance, compared to the existing schemes that do not consider the effects of network dynamics on the expected long-term rewards, while scaling reasonably well as the network size increases.
This work is partially supported by the National Science Foundation under Grants CNS-1910348 and CNS-1822087, and InterDigital Communications, Inc.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Patel, M., Naughton, B., Chan, C., Sprecher, N., Abeta, S., Neal, A., et al.: Mobile-edge computing introductory technical white paper. White Paper, Mobile-Edge Computing (MEC) Industry Initiative (2014)
Liu, H., Eldarrat, F., Alqahtani, H., Reznik, A., de Foy, X., Zhang, Y.: Mobile edge cloud system: architectures, challenges, and approaches. IEEE Syst. J. 12(3), 2495–2508 (2018)
Aazam, M., Huh, E.-N.: Dynamic resource provisioning through fog micro datacenter. In: Proceedings of IEEE PerCom Workshops, St. Louis, MO, pp. 105–110, March 2015
Zhang, H., Xiao, Y., Bu, S., Niyato, D., Yu, F.R., Han, Z.: Computing resource allocation in three-tier IoT fog networks: a joint optimization approach combining stackelberg game and matching. IEEE Internet Things J. 4(5), 1204–1215 (2017)
Xiao, Y., Krunz, M.: QoE and power efficiency tradeoff for fog computing networks with fog node cooperation. In: Proceedings of IEEE INFOCOM 2017, Atlanta, GA, May 2017
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016), pp. 2094–2100, February 2016
Bertsekas, D.P.: Dynamic Programming and Optimal Control. Athena Scientific, Belmont (1995)
Puterman, M.L., Shin, M.C.: Modified policy iteration algorithms for discounted Markov decision problems. Manag. Sci. 24(11), 1127–1137 (1978)
Howard, R.: Dynamic Programming and Markov Processes. MIT Press, Cambridge (1960)
Tsitsiklis, J.N., van Roy, B.: Feature-based methods for large scale dynamic programming. Mach. Learn. 22(1–3), 59–94 (1996)
Chen, X., et al.: Multi-tenant cross-slice resource orchestration: a deep reinforcement learning approach. IEEE J. Selected Areas Commun. (JSAC) 37(10), 2377–2392 (2019)
Acknowledgements
Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Hsieh, LT., Liu, H., Guo, Y., Gazda, R. (2020). Quality of Service Optimization in Mobile Edge Computing Networks via Deep Reinforcement Learning. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12384. Springer, Cham. https://doi.org/10.1007/978-3-030-59016-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-59016-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59015-4
Online ISBN: 978-3-030-59016-1
eBook Packages: Computer ScienceComputer Science (R0)