Abstract
In recent years, the Internet of Things (IoT) has been growing rapidly, and new applications using the IoT includes autonomous driving systems, mobile health, smart homes, VR/AR technologies, and many more. While IoT applications plays important roles in enriching our lives, the size of each generated task is increasing compared to tasks generated by traditional mobile devices, which can cause high latency when deployed in traditional cloud computing. Edge computing has emerged as a method to mitigate this problem. Edge computing can provide services with lower latency than conventional cloud computing methods because the tasks are processed in the vicinity of the user’s device. However, when the tasks are concentrated in one of the distributed servers, the low processing power of these servers becomes a bottleneck and high latency may occur.
In this paper, we proposed a deep reinforcement learning based offloading mechanism in an edge computing environment which can dynamically offload a task based on the performance and availability of nearby edge servers. Preliminary experiment results are promising and offered insights on related issues.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ashton, K.: That “Internet of Things.” Thing Adv. Internet Things 6(4), 97–101 (2009)
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)
Gao, Z., Hao, W., Han, Z., Yang, S.: Q-learning-based task offloading and resources optimization for a collaborative computing system. IEEE Access 8, 149011–149024 (2020)
Mochizuki, D., Abiko, Y., Mineno, H., Saito, T., Ikeda, D., Katagiri, M.: Deep reinforcement learning-based method of mobile data offloading. In: 2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU), No. 18493208, Auckland, New Zealand, 5–8 October (2018)
Shino, Y., Sasai, K., Kitagata, G., Kinoshita, T.: Edge computing using autonomous decentralized task offload. IPSJ Res. Rep. 2017-MBL-85(26), 1–7 (2017)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (2014, 2015)
Makino, T., et al.: Future Strengthening Learning. Morikita Publishing (October 2016)
Kimura, G.: Basics of Reinforcement Learning. J-STAGE 52(1), 72–77 (2013)
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., Riedmiller, M.: Deterministic policy gradient algorithms. In: 31st International Conference on Machine Learning, vol. 32, pp. 387–395 (June 2014)
Geron, A. (Author), Shimoda, R. (Translation), Nagao, T. (Translation): Practical Machine Learning with Scikit-Learn, Keras, TensorFlow. O’Reilly, Japan (October 2020). (Translated in Japanese)
Szepesvari, C. (Author), Oyamada, S. (Editor), Maeda, S., Koyama, M. (Translation): Algorithms for Reinforcement Learning Rapid Learning-Basic Theory and Algorithms, pp. 8–12, Kyoritsu Shuppan, Tokyo (July 2017)
Volodymvr, M., et al.: Playing Atari with Deep Reinforcement Learning. arXiv: 1312.5602 (2013)
Volodymvr, M., et al.: Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015)
Lillicrap, T.P., et al.: Continuous control with Deep Reinforcement Learning. arXiv: 1509.02971 (July 2019). (latest version)
Long-Ji, L.: Reinforcement Learning for Robots Using Neural Networks. No. CMU-CS-93–103, Carnegie-Mellon University PA School of Computer Science (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ide, S., Apduhan, B.O. (2022). A Framework of an RL-Based Task Offloading Mechanism for Multi-users in Edge Computing. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13379. Springer, Cham. https://doi.org/10.1007/978-3-031-10545-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-10545-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10544-9
Online ISBN: 978-3-031-10545-6
eBook Packages: Computer ScienceComputer Science (R0)