Abstract
Fog Computing (FC) is an extension of cloud computing, providing services closer to the users near the edge. In FC, the Mobile Devices (MDs) can offload heavy tasks to an edge or cloud server. The decision of whether a MD offloads tasks to a Mobile Edge Computing (MEC) or a Mobile Cloud Computing (MCC) servers should be carefully studied. In this paper, an Advanced Deep Reinforcement Learning (ADRL) protocol is proposed to improve task offloading. It can generate multi-class offload decisions for executing independent tasks locally, at the edge, or cloud while considering the current workload of MD, MEC, and MCC to maintain balance in the mobile system. The experiments show the efficiency of the proposed algorithm in terms of the computation time and convergence to the optimal solution.
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
References
Wang, B., Wang, C., Huang, W., Song, Y., Qin, X.: A survey and taxonomy on task offloading for edge-cloud computing. IEEE 8, 186080–186101 (2020)
Abdel-Kader, R.F., El-Sayad, N.E., Rizk, R.Y.: Efficient energy and completion time for dependent task computation offloading algorithm in industry 4.0. PLoS ONE 16(6), e0252756 (2021)
Nashaat, H., Ahmed, E., Rizk, R.: IoT application placement algorithm based on multi-dimensional QoE prioritization model in fog computing environment. IEEE Access 8, 1–12 (2020)
Sarker, I.H.: Machine learning: algorithms, real-world applications and research directions. SN Comput. Sci. 2(3), 160 (2021)
Lei, C.: Deep reinforcement learning. In: Deep Learning and Practice with MindSpore. Cognitive Intelligence and Robotics. Springer, Singapore, pp. 217–243 (2021)
Heuillet, A., Couthouis, F., RodrÃguez, N.D.: Explainability in deep reinforcement learning. Knowl.-Based Syst. 214, 106685 (2021)
Du, M., Wang, Y., Ye, K., Xu, C.: Algorithmics of cost-driven computation offloading in the edge-cloud environment. IEEE Trans. Comput. 69(10), 1519–1532 (2020)
Kai, C., Zhou, H., Yi, Y., Huang, W.: Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability. IEEE Trans. Cogn. Commun. Netw. 7(2), 624–634 (2021)
Yu, S., Wang, X., Langar, R.: Computation offloading for mobile edge computing: a deep learning approach. In: IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), IEEE, pp. 1–6, October 2017
Rahbari, D., Nickray, M.: Task offloading in mobile fog computing by classification and regression tree. Peer-to-Peer Peer Netw. Appl. 13, 104–122 (2020)
Wang, X., Xu, W., Jin, Z.: A hidden Markov model-based dynamic scheduling approach for mobile cloud telemonitoring. In: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics, BHI. IEEE, pp. 273–276, February 2017
Sheng, J., Hu, J., Teng, X., Wang, B., Pan, X.: Computation offloading strategy in mobile edge computing. Information 10(6), 2 (2019)
Huang, L., Feng, X., Zhang, C., Qian, L., Wu, Y.: Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing. Digit. Commun. Netw. 5(1), 10–17 (2019)
Alam, M.G.R., Hassan, M.M., Uddin, M.Z., Almogren, A., Fortino, G.: Autonomic computation offloading in mobile edge for IoT applications. Future Gener. Comput. Syst. 90, 149–157 (2019)
Khayyat, M., Elgendy, I.A., Muthanna, A., Alshahrani, A.S., Alharbi, S., Koucheryavy, A.: Advanced deep-learning-based computational offloading for multilevel vehicular edge-cloud computing networks. IEEE Access 8, 137052–137062 (2020)
Huang, L., Bi, S., Zhang, Y.-J.A.: Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks. IEEE Trans. Mobile Comput. 19(11), 2581–2593 (2020)
Huang, L., Feng, X., Feng, A., Huang, Y., Qian, P.: Distributed deep learning-based offloading for mobile edge computing networks. Mobile Netw. Appl. (2018)
Huang, L., Feng, X., Zhang, L., Qian, L., Wu, Y.: Multi-server multi-user multi-task computation offloading for mobile edge computing networks. Sensors 19(6), 1446 (2019)
Liang, F., Yu, W., Liu, X., Griffith, D., Golmie, N.: Towards edge-based deep learning in industrial Internet of Things. IEEE Internet Things J. 7(5), 4329-4341 (2020)
Wu, H., Zhang, Z., Guan, C., Wolter, K., Xu, M.: Collaborate edge and cloud computing with distributed deep learning for smart city Internet of Things. IEEE Internet Things J. 7(9), 8099–8110 (2020)
Abadi, M., Agarwal, A., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467 (2016)
Carneiro, T., Medeiros Da NóBrega, R.V., Nepomuceno, T., Bian, G., De Albuquerque, V.H.C., Filho, P.P.R.: Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access 6, 61677–61685 (2018)
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
Hashem, W., Attia, R., Nashaat, H., Rizk, R. (2022). Advanced Deep Reinforcement Learning Protocol to Improve Task Offloading for Edge and Cloud Computing. In: Hassanien, A.E., Rizk, R.Y., Snášel, V., Abdel-Kader, R.F. (eds) The 8th International Conference on Advanced Machine Learning and Technologies and Applications (AMLTA2022). AMLTA 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-031-03918-8_51
Download citation
DOI: https://doi.org/10.1007/978-3-031-03918-8_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-03917-1
Online ISBN: 978-3-031-03918-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)