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Offloading strategy with PSO for mobile edge computing based on cache mechanism

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Abstract

With the development of Internet of Things (IoT) devices and the growth of users’ demand for computation and real-time services, artificial intelligence has been applied to reduce the system cost for future network systems. To meet the demand of network services, the paradigm of edge networks is increasingly shifting towards the joint design of computation, communication and caching services. This paper investigates a multi-user cache-enabled mobile edge computing (MEC) network and proposes an intelligent particle swarm optimization (PSO) based offloading strategy with cache mechanism. In each time slot, the server selects one file among multiple ones to pre-store, according to the proposed cache replacement strategy. In the next time slot, the requested files by the users needn’t to be computed and offloaded, if these files have been cached in the server. For the files that have not been cached in the server, PSO algorithm is adopted to find an appropriate offloading ratio to implement the partial offloading. Simulation results are finally presented to validate the proposed studies. In particular, we can find that incorporating the proposed cache replacement strategy into the computation offloading can effectively reduce the system latency and energy consumption for the future networks.

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Data availability

The authors state the data availability in this manuscript.

Code availability

Not applicable.

Abbreviations

MEC:

Mobile edge computing

BSs:

Base stations

PSO:

Particle swarm optimization

UAV:

Unmanned aerial vehicle

ES:

Edge server

FIFO:

First in first out

References

  1. Xie, H., Gao, F., Zhang, S., Jin, S.: A unified transmission strategy for TDD/FDD massive MIMO systems with spatial basis expansion model. IEEE Trans. Veh. Technol. 66(4), 3170–3184 (2017)

    Article  Google Scholar 

  2. Hu, X., Zhong, C., Zhu, Y., Chen, X., Zhang, Z.: Programmable metasurface-based multicast systems: design and analysis. IEEE J. Sel. Areas Commun. 38(8), 1763–1776 (2020)

    Article  Google Scholar 

  3. Kumar, B.A., Rao, P.T.: Optimized design and analysis approach of user detection by non cooperative detection computing methods in CR networks. Clust. Comput. 22(4), 9777–9785 (2019)

    Google Scholar 

  4. Zhao, J., Liu, J.: Future 5G-oriented system for urban rail transit: opportunities and challenges. IEEE Trans. Veh. Technol. 18(2), 1–12 (2021)

    Google Scholar 

  5. Lai, X.: Secure mobile edge computing networks in the presence of multiple eavesdroppers. IEEE Trans. Commun. 1–12 (2021)

  6. Zhao, J., Li, Q., Gong, Y., Zhang, K.: Computation offloading and resource allocation for cloud assisted mobile edge computing in vehicular networks. IEEE Trans. Veh. Technol. 68(8), 7944–7956 (2019)

    Article  Google Scholar 

  7. Khan, S.I., Qadir, Z., Munawar, H.S., Nayak, S.R., Budati, A.K., Verma, K., Prakash, D.: UAVs path planning architecture for effective medical emergency response in future networks. Phys. Commun. 47, 101337 (2021)

  8. Chen, L.: Intelligent ubiquitous computing for future UAV-enabled MEC network systems. Clust. Comput. 99, 1–8 (2021)

  9. Lai, S.: Intelligent secure mobile edge computing for beyond 5G wireless networks. Phys. Commun. 45(101283), 1–8 (2021)

    Google Scholar 

  10. Guo, Y.: Efficient and flexible management for industrial internet of things: a federated learning approach. Comput. Netw. 192, 1–9 (2021)

    Article  Google Scholar 

  11. Lai, S., Guo, Y.: Distributed machine learning for multiuser mobile edge computing systems. IEEE J. Sel. Top. Signal Process. 99, 1–12 (2021)

  12. Li, C.: Dynamic offloading for multiuser muti-CAP MEC networks: a deep reinforcement learning approach. IEEE Trans. Veh. Technol. 70(3), 2922–2927 (2021)

    Article  Google Scholar 

  13. Zhao, R.: Deep reinforcement learning based mobile edge computing for intelligent internet of things. Phys. Commun. 43, 1–7 (2020)

    Google Scholar 

  14. He, K.: Learning based signal detection for MIMO systems with unknown noise statistics. IEEE Trans. Commun. 69, 3025–3038 (2021)

    Article  Google Scholar 

  15. He, K.: Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT. Phys. Commun. 43, 1–7 (2020)

    Article  Google Scholar 

  16. Tang, S.: Dilated convolution based CSI feedback compression for massive MIMO systems. IEEE Trans. Veh. Technol. 99, 1–5 (2021)

  17. Xia, J., Fan, L.: Computational intelligence and deep reinforcement learning for next-generation industrial IoT. IEEE Trans. Netw. Sci. Eng. 99, 1–12 (2021)

  18. Tang, S.: Battery-constrained federated edge learning in UAV-enabled IoT for B5G/6G networks. Phys. Commun. 47(101381), 1–9 (2021)

    Google Scholar 

  19. Zhao, Z.: System optimization of federated learning networks with a constrained latency. IEEE Trans. Veh. Technol. 99, 1–5 (2021)

  20. Wang, X., Chen, M., Taleb, T., Ksentini, A., Leung, V.C.M.: Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun. Mag. 52(2), 131–139 (2014)

    Article  Google Scholar 

  21. Xia, J.: Secure cache-aided multi-relay networks in the presence of multiple eavesdroppers. IEEE Trans. Commun. 67(11), 7672–7685 (2019)

    Article  Google Scholar 

  22. Bi, S., Huang, L., Zhang, Y.A.: Joint optimization of service caching placement and computation offloading in mobile edge computing systems. IEEE Trans. Wirel. Commun. 19(7), 4947–4963 (2020)

    Article  Google Scholar 

  23. Hu, X., Zhong, C., Zhang, Y., Chen, X., Zhang, Z.: Location information aided multiple intelligent reflecting surface systems. IEEE Trans. Commun. 68(12), 7948–7962 (2020)

    Article  Google Scholar 

  24. He, L., He. K.: Towards optimally efficient search with deep learning for large-scale MIMO systems. IEEE Trans. Commun. (99), 1-12 (2022)

  25. Wang, Y., Adachi, F.: Multi-task learning for generalized automatic modulation classification under non-Gaussian noise with varying SNR conditions. IEEE Trans. Wirel. Commun. (2021). https://doi.org/10.1109/TWC.2021.3052222

  26. Hu, X., Wang, J., Zhong, C.: Statistical CSI based design for intelligent reflecting surface assisted MISO systems. Sci. China: Inf. Sci. 63(12), 222303 (2020)

  27. Li, X., Mengyan, H., Liu, Y., Menon, VG., Paul, A., Ding, Z.: I/Q imbalance aware nonlinear wireless-powered relaying of B5G networks: security and reliability analysis. IEEE Trans. Netw. Sci. Eng. (2020). https://doi.org/10.1109/TNSE.2020.3020950

  28. Na, Z., Wang, J., Liu, C., Guan, M., Gao, Z.: Join trajectory optimization and communication design for UAV-enabled OFDM networks. Ad Hoc Netw. 98, 102031 (2020). https://doi.org/10.1016/j.adhoc.2019.102031

  29. Ale, L., Zhang, N., Fang, X., Chen, X., Wu, S., Li, L.: Delay-aware and energy-efficient computation offloading in mobile edge computing using deep reinforcement learning. IEEE Trans. Cogn. Commun. Netw. (2021). https://doi.org/10.1109/TCCN.2021.3066619

  30. Zhang, J., Hu, X., Ning, Z., Ngai, E.C.H., Zhou, L., Wei, J., Cheng, J., Hu, B.: Energy-latency tradeoff for energy-aware offloading in mobile edge computing networks. IEEE Internet Things J. 5(4), 2633–2645 (2017)

    Article  Google Scholar 

  31. Zhang, W., Wen, Y., Guan, K., Kilper, D., Luo, H., Wu, D.O.: Energy-optimal mobile cloud computing under stochastic wireless channel. IEEE Trans. Wirel. Commun. 12(9), 4569–4581 (2013)

    Article  Google Scholar 

  32. Zhao, Z., Zhao, R., Xia, J., Lei, X., Li, D., Yuen, C., Fan, L.: A novel framework of three-hierarchical offloading optimization for MEC in industrial IoT networks. IEEE Trans. Ind. Inform. 16(8), 5424–5434 (2020). https://doi.org/10.1109/TII.2019.2949348

    Article  Google Scholar 

  33. Yadav, R., Zhang, W., Kaiwartya, O., Song, H., Yu, S.: Energy-latency tradeoff for dynamic computation offloading in vehicular fog computing. IEEE Trans. Veh. Technol. 69(12), 14198–14211 (2020). https://doi.org/10.1109/TVT.2020.3040596

    Article  Google Scholar 

  34. Chen, B., Yang, C.: Caching policy for cache-enabled D2D communications by learning user preference. IEEE Trans. Commun. 66(12), 6586–6601 (2018)

    Article  Google Scholar 

  35. Cheng, C., Guo, L.: Machine learning-aided trajectory prediction and conflict detection for internet of aerial vehicles. IEEE Internet Things J. (2021). https://doi.org/10.1109/JIOT.2021.3060904

  36. Li, X., Wang, Q., Liu, M., Li, J., Peng, H., Piran, M.J., Li, L.: Cooperative wireless-powered noma relaying for B5G IoT networks with hardware impairments and channel estimation errors. IEEE Internet Things J. 8(7), 5453–5467 (2021). https://doi.org/10.1109/JIOT.2020.3029754

    Article  Google Scholar 

  37. Na, Z., Liu, Y., Shi, J., Liu, C., Gao, Z.: UAV-supported clustered NOMA for 6G-enabled internet of things: trajectory planning and resource allocation. IEEE Internet Things J. (2020). https://doi.org/10.1109/JIOT.2020.3004432

    Article  Google Scholar 

  38. Wang, Z.: An adaptive deep learning-based UAV receiver design for coded MIMO with correlated noise. Phys. Commun. 45(101295), 1–8 (2021)

    Google Scholar 

  39. Li, X., Zhao, M., Zeng, M., Mumtaz, S., Menon, V.G., Ding, Z., Dobre, O.A.: Hardware impaired ambient backscatter NOMA systems: reliability and security. IEEE Trans. Commun. 69(4), 2723–2736 (2021). https://doi.org/10.1109/TCOMM.2021.3050503

    Article  Google Scholar 

  40. Xia, J., Deng, D., Fan, D.: A note on implementation methodologies of deep learning-based signal detection for conventional MIMO transmitters. IEEE Trans. Broadcast. 66(3), 744–745 (2020)

    Article  Google Scholar 

  41. Yang, J., Ruan, D., Huang, J., Kang, X., Shi, Y.: An embedding cost learning framework using GAN. IEEE Trans. Inf. Forensics Secur. 15, 839–851 (2020)

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by the International Science and Technology Cooperation Projects of Guangdong Province (No. 2020A0505100060), Natural Science Foundation of Guangdong Province (Nos. 2021A1515011392/2021A1515011812), and in part by the research program of Guangzhou University (No. YK2020008/YJ2021003).

Funding

This work was supported in part by the International Science and Technology Cooperation Projects of Guangdong Province (No. 2020A0505100060), Natural Science Foundation of Guangdong Province (Nos. 2021A1515011392/2021A1515011812), and in part by the research program of Guangzhou University (No. YK2020008/YJ2021003).

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Authors and Affiliations

  1. School of Computer Science, Guangzhou University, Guangzhou, China

    Wenqi Zhou, Shunpu Tang, Lijia Lai, Junjuan Xia & Liseng Fan

  2. School of Electronics and Communication Engineering, Guangzhou University, Guangzhou, China

    Lunyuan Chen & Fasheng Zhou

Authors
  1. Wenqi Zhou
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  2. Lunyuan Chen
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  3. Shunpu Tang
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  4. Lijia Lai
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  5. Junjuan Xia
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  6. Fasheng Zhou
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  7. Liseng Fan
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Contributions

WZ designed the proposed offloading and caching strategy. LC helped conduct the simulation and verify the results. ST, LL and JX analyzed the application scenarios of the model, arranged the data, and proof read this paper. FZ was responsible for the check of the full paper and simulations. LF was responsible for checking the critical steps in the strategy design and improving the presentation style of the whole paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Fasheng Zhou or Liseng Fan.

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Zhou, W., Chen, L., Tang, S. et al. Offloading strategy with PSO for mobile edge computing based on cache mechanism. Cluster Comput 25, 2389–2401 (2022). https://doi.org/10.1007/s10586-021-03414-0

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