Multi-user task offloading for mobile edge computing based on reinforcement learning

Nandhini Jembu Mohanram, Saravanan Kaliaperumal, Anuratha Kesavan, Uma Sankar

Abstract


Mobile Edge computing (MEC) enables network functions and control programmable and operates key constituents of social networks in terms of increasing user’s support on devices to carry out compute. It requires traffic offloading and task scheduling to improve the storage and fast computing.  In this paper, a novel method, including data driven traffic modeling enabled by a Reinforcement learning algorithm (RLTOA), is proposed for offloading traffic and improving the computing speed and minimizing the application latency of the social network. The result of the proposed data driven modeling is compared with existing methods and validate how the data driven traffic modeling for providing the computation offloading service in terms of energy budget and the mobile drop and execution of edge server. The presented computation offloading, and energy management solutions can provide valuable perceptions for practical applications of MEC. Extensive numerical findings are presented to endorse the efficacy of RLTOA and display the effect of the social network requirement.


Keywords


MEC; Reinforcement Learning; Traffic offloading; Task scheduling;

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References:

Peng Wei et al, “Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence” IEEE Access, Vol.10, 2022, DOI 10.1109/ACCESS.2022.3183647

H. Sami, H. Otrok, J. Bentahar, and A. Mourad, “AI-based resource provisioning of IoE services in 6G: A deep reinforcement learning approach,” IEEE Trans. Netw. Service Manage., vol. 18, no. 3, pp. 3527–3540, Sep. 2021

P. Zhou, Y. Xie, B. Niu, L. Pu, Z. Xu, H. Jiang, and H. Huang, “QoEaware 3D video streaming via deep reinforcement learning in software defined networking enabled mobile edge computing,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 1, pp. 419–433, Jan. 2021.

Y. Kunpeng, H. Shan, T. Sun, R. Hu, Y. Wu, L. Yu, Z. Zhang, and T. Q. S. Quek, “Reinforcement learning-based mobile edge computing and transmission scheduling for video surveillance,” IEEE Trans. Emerg. Topics Comput., vol. 10, no. 2, pp. 1142–1156, Apr./Jun. 2021.

Samuel, Amalorpava Mary Rajee, Yamuna Devi MM, and S. Madhusudhanan. "Multi-agent Task Assignment in Unmanned Aerial Vehicle Edge Computing based on Deep Learning Approach." 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2024.

Amalorpava Mary Rajee,S., Merline.A.: Machine Intelligence Technique for Blockage Effects in Next-Generation Heterogeneous Networks,Radio Engineering, Vol.29, Issue 3, Sep 2020.

B. Batagelj, L. Pavlovic, L. Naglic and S. Tomazic, ''Convergence of fixed and mobile networks by radio over fibre technology'', Informacije MIDEM, vol. 41 (2011), no. 2, pp. 144-149

P. Ramamoorthy, K. Ramanathan, ''A Novel Method for 5Generation Multiple-Input, Multiple-Output Orthogonal Frequency-Division Multiplexing using Cauchy Evading Golden Tortoise Adaptive-Bi Directional-Long Short-Term Memory'', Informacije MIDEM, vol. 54 (2024), no. 3, pp. 201-213

A. Bozorgchenani, F. Mashhadi, D. Tarchi and S. A. Salinas Monroy, "Multi-Objective Computation Sharing in Energy and Delay Constrained Mobile Edge Computing Environments," in IEEE Transactions on Mobile Computing, vol. 20, no. 10, pp. 2992-3005, 1 Oct. 2021, doi: 10.1109/TMC.2020.2994232.

M. Peng, D. Liang, Y. Wei, J. Li, and H. Chen, “Self-configuration and self-optimization in LTE-advanced heterogeneous networks," IEEE Commun. Mag., vol. 51, no. 5, pp.36-45, May 2013.

X. Xia et al., "OL-MEDC: An Online Approach for Cost-Effective Data Caching in Mobile Edge Computing Systems," in IEEE Transactions on Mobile Computing, vol. 22, no. 3, pp. 1646-1658, 1 March 2023, doi: 10.1109/TMC.2021.3107918

T. Mlinar, S. Tomažič, B. Batagelj, Centimeter positioning accuracy in modern wireless cellular networks – wish or reality?'', Informacije MIDEM, vol. 53 (2023), no. 4, pp. 239-248

Z. Gao, B. Wen, L. Huang, C. Chen, and Z. Su, “Q-learning-based power control for LTE enterprise femtocell networks," IEEE Syst. J., vol. 11, no. 4, pp. 2699-2707, Dec 2017.

Roohollah Amiri, Mojtaba Ahmadi Almasi, Jeffrey G. Andrews,Hani Mehrpouyan, “Reinforcement Learning for self organization and power control of Two-Tier Heterogeneous Networks,IEEE Transactions on Wireless Communications, 2019, available online at. arXiv:1812.09778v2 [cs.IT]

Mar 2019 15. T. Wang, A. Hussain, L. Zhang, and C. Zhao, “Collaborative edge computing for social internet of vehicles to alleviate traffic congestion,” IEEE Trans. Computat. Social Syst., vol. 9, no. 1, pp. 184–196, Feb. 2022.

H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, “Learning to optimize: Training deep neural networks for interference management," IEEE Trans. Signal Processing, vol. 66, no. 20, pp. 5438-5453, Oct 2018.

S. Niknam, R. Barazideh, and B. Natarajan, “Cross-layer Interference Modeling for 5G MmWave Networks in the Presence of Blockage," ArXiv e-prints, Jul. 2018.

P. V. Klaine, M. A. Imran, O. Onireti, and R. D. Souza,“A survey of machine learning techniques applied to self-organizing cellular networks," IEEE Commun. Surv. Tutor.,vol. 19, no. 4, pp. 2392-2431, Fourthquarter 2017

R. Li, Z. Zhao, X. Zhou, G. Ding, Y. Chen, Z. Wang, and H.Zhang, “Intelligent 5G: When cellular networks meet artificial intelligence," IEEE Wirel. Commun., vol. 24, no. 5, pp. 175-183,Oct 2017.

M. Weichold, M. Hamdi, M. Z. Shakir, M. Abdallah, G. K.Karagiannidis,and M. Ismail, Eds., Cognitive Aware Interference Mitigation Scheme for LTE Femtocells. Cham: Springer International Publishing, 2015, pp. 607 Available:http://dx.doi.org/10.1007/978-3-319-24540-9 50.

K. Guo, R. Gao, W. Xia, and T. Q. S. Quek, “Online learning based computation offloading in MEC systems with communication and computation dynamics,” IEEE Trans. Commun., vol. 69, no. 2, pp. 1147–1162, Feb. 2021

C.Kai et al. Collaborative cloud-edge-end task offloading in mobile-edge computing networks with limited communication capability” IEEE Trans. Cogn. Commun. Netw. (2020)

A.M.R. Samuel and M. Arulraj, "Performance analysis of flexible indoor and outdoor user distribution in urban multi-tier heterogeneous network", Int. J. Mob. Commun, vol. 21, no. 1, pp. 119-133, 2023.

Ramesh, Parameswaran, Bhuvaneswari Mohan, Lavanya Viswanath, and Bino Jesu Stephen. "Software Defined Network Architecture Based Network Slicing in Fifth Generation Networks." Informacije MIDEM 54, no. 2 (2024).

S. A. M. Rajee, A. Merline, and M. M. Y. Devi, “Game theoretic model for power optimization in next-generation heterogeneous network,” Signal Image and Video Processing., vol. 17, no. 7, pp. 3721– 3729, Oct. 2023, doi: 10.1007/s11760-023-02599-8.




DOI: https://doi.org/10.33180/InfMIDEM2025.305

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