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Energy efficient resource allocation based on virtual network embedding for IoT data generation

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Abstract

The Internet of Things (IoT) has become a core driver leading technological advancements and social transformations. Furthermore, data generation plays multiple roles in IoT, such as driving decision-making, achieving intelligence, promoting innovation, improving user experience, and ensuring security, making it a critical factor in promoting the development and application of IoT. Due to the vast scale of the network and the complexity of device interconnection, effective resource allocation has become crucial. Leveraging the flexibility of Network Virtualization technology in decoupling network functions and resources, this work proposes a Multi-Domain Virtual Network Embedding algorithm based on Deep Reinforcement Learning to provide energy-efficient resource allocation decision-making for IoT data generation. Specifically, we deploy a four-layer structured agent to calculate candidate IoT nodes and links that meet data generation requirements. Moreover, the agent is guided by the reward mechanism and gradient back-propagation algorithm for optimization. Finally, the effectiveness of the proposed method is validated through simulation experiments. Compared with other methods, our method improves the long-term revenue, long-term resource utilization, and allocation success rate by 15.78%, 15.56%, and 6.78%, respectively.

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References

  • Ahmed, E., Yaqoob, I., Hashem, I.A.T., Khan, I., Ahmed, A.I.A., Imran, M., Vasilakos, A.V.: The role of big data analytics in internet of things. Comput. Netw. 129, 459–471 (2017)

    Article  Google Scholar 

  • Al-Hadhrami, Y., Hussain, F.K.: Real time dataset generation framework for intrusion detection systems in IoT. Futur. Gener. Comput. Syst. 108, 414–423 (2020)

    Article  Google Scholar 

  • Chen, X., Wang, L., Li, C., Yang, J., Lu, Z., Lu, G., Gu, Y., Jiang, Y.: Resource distribution equilibrium for virtual network embedding over flexi-grid optical networks. J. Lightwave Technol. 39(15), 4894–4908 (2021)

    Article  Google Scholar 

  • Chen, N., Zhang, P., Kumar, N., Hsu, C.-H., Abualigah, L., Zhu, H.: Spectral graph theory-based virtual network embedding for vehicular fog computing: a deep reinforcement learning architecture. Knowl.-Based Syst. 257, 109931 (2022)

    Article  Google Scholar 

  • Chen, N., Shen, S., Duan, Y., Huang, S., Zhang, W., Tan, L.: Non-euclidean graph-convolution virtual network embedding for space-air-ground integrated networks. Drones 7(3), 165 (2023)

    Article  Google Scholar 

  • Chen, N., Shen, S., Duan, Y., Huang, S., Zhang, W., Tan, L.: Non-euclidean graph-convolution virtual network embedding for space-air-ground integrated networks. Drones 7(3), 165 (2023). https://doi.org/10.3390/drones7030165

    Article  Google Scholar 

  • Cheng, X., Su, S., Zhang, Z., Wang, H., Yang, F., Luo, Y., Wang, J.: Virtual network embedding through topology-aware node ranking. ACM SIGCOMM Comput. Commun. Rev. 41(2), 38–47 (2011)

    Article  Google Scholar 

  • Chettri, L., Bera, R.: A comprehensive survey on internet of things (IoT) toward 5G wireless systems. IEEE Internet Things J. 7(1), 16–32 (2020). https://doi.org/10.1109/JIOT.2019.2948888

    Article  Google Scholar 

  • Congress, S., Puppala, A.: Eye in the sky: condition monitoring of transportation infrastructure using drones. In: Proceedings of the Institution of Civil Engineers-Civil Engineering, vol. 176, pp. 40–48 (2022). Thomas Telford Ltd

  • Dash, S., Yale, A., Guyon, I., Bennett, K.P.: Medical time-series data generation using generative adversarial networks. In: Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25–28, 2020, Proceedings 18, pp. 382–391 (2020). Springer

  • Du, H., Wang, J., Niyato, D., Kang, J., Xiong, Z., Guizani, M., Kim, D.I.: Rethinking wireless communication security in semantic internet of things. IEEE Wirel. Commun. 30(3), 36–43 (2023). https://doi.org/10.1109/MWC.011.2200547

    Article  Google Scholar 

  • Duan, Y., Chen, N., Shen, S., Zhang, P., Qu, Y., Yu, S.: FDSA-STG: Fully dynamic self-attention spatio-temporal graph networks for intelligent traffic flow prediction. IEEE Trans. Veh. Technol. 71(9), 9250–9260 (2022). https://doi.org/10.1109/TVT.2022.3178094

    Article  Google Scholar 

  • Duan, Y., Chen, N., Zhang, P., Kumar, N., Chang, L., Wen, W.: MS2GAH: multi-label semantic supervised graph attention hashing for robust cross-modal retrieval. Pattern Recogn. 128, 108676 (2022)

    Article  Google Scholar 

  • Duan, Y., Chen, N., Bashir, A.K., Alshehri, M.D., Liu, L., Zhang, P., Yu, K.: A web knowledge-driven multimodal retrieval method in computational social systems: unsupervised and robust graph convolutional hashing. IEEE Trans. Comput. Soc. Syst. (2022). https://doi.org/10.1109/TCSS.2022.3216621

    Article  Google Scholar 

  • Feng, Q., Li, D., Wu, Z.: A data envelopment analysis approach for resource allocation and reallocation. IEEE Trans. Eng. Manage. 71, 3295–3307 (2024). https://doi.org/10.1109/TEM.2023.3340302

    Article  Google Scholar 

  • Hsu, I., Huang, K.-H., Boschee, E., Miller, S., Natarajan, P., Chang, K.-W., Peng, N., et al.: Degree: a data-efficient generation-based event extraction model. arXiv preprint arXiv:2108.12724 (2021)

  • Liu, Y., Zhang, J.: Service function chain embedding meets machine learning: deep reinforcement learning approach. IEEE Trans. Netw. Serv. Manage. (2024). https://doi.org/10.1109/TNSM.2024.3353808

    Article  Google Scholar 

  • Sun, G., Li, Y., Yu, H., Vasilakos, A.V., Du, X., Guizani, M.: Energy-efficient and traffic-aware service function chaining orchestration in multi-domain networks. Futur. Gener. Comput. Syst. 91, 347–360 (2019)

    Article  Google Scholar 

  • Wang, L., Zheng, Z., Chen, N., Chi, Y., Liu, Y., Zhu, H., Zhang, P., Kumar, N.: Multi-target-aware energy orchestration modeling for grid 2.0: a network virtualization approach. IEEE Access 11, 21699–21711 (2023). https://doi.org/10.1109/ACCESS.2023.3251698

    Article  Google Scholar 

  • Woodbridge, J., Anderson, H.S., Ahuja, A., Grant, D.: Predicting domain generation algorithms with long short-term memory networks. arXiv preprint arXiv:1611.00791 (2016)

  • Wu, S., Chen, N., Xiao, A., Jia, H., Jiang, C., Zhang, P.: AI-enabled deployment automation for 6G space-air-ground integrated networks: challenges, design, and outlook. IEEE Netw. (2024). https://doi.org/10.1109/MNET.2024.3368753

    Article  Google Scholar 

  • Wu, S., Chen, N., Xiao, A., Zhang, P., Jiang, C., Zhang, W.: AI-empowered virtual network embedding: a comprehensive survey. IEEE Commun. Surv. Tutor. (2024). https://doi.org/10.1109/COMST.2024.3424533

    Article  Google Scholar 

  • Wu, S., Chen, N., Wen, G., Xu, L., Zhang, P., Zhu, H.: Virtual network embedding for task offloading in IIoT: a DRL-assisted federated learning scheme. IEEE Trans. Industr. Inf. (2024). https://doi.org/10.1109/TII.2024.3353848

    Article  Google Scholar 

  • Xiao, A., Chen, N., Wu, S., Zhang, P., Cao, S., Jiang, C.: DNFS-VNE: deep neuro fuzzy system driven virtual network embedding. arXiv e-prints, 2310 (2023)

  • Yao, Y., Zheng, L., Yang, X., Naphade, M., Gedeon, T.: Simulating content consistent vehicle datasets with attribute descent. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, pp. 775–791 (2020). Springer

  • Yao, H., Ma, S., Wang, J., Zhang, P., Jiang, C., Guo, S.: A continuous-decision virtual network embedding scheme relying on reinforcement learning. IEEE Trans. Netw. Serv. Manage. 17(2), 864–875 (2020)

    Article  Google Scholar 

  • Zhan, K., Chen, N., Santhosh Kumar, S.V.N., Kibalya, G., Zhang, P., Zhang, H.: Edge computing network resource allocation based on virtual network embedding. Int. J. Commun. Syst. (2022). https://doi.org/10.1002/dac.5344

    Article  Google Scholar 

  • Zhang, Y., Li, C., Chen, N., Zhang, P.: Intelligent requests orchestration for microservice management based on blockchain in software defined networking: a security guarantee. In: 2022 IEEE International Conference on Communications Workshops (ICC Workshops), pp. 254–259 (2022). IEEE

  • Zhang, P., Wang, C., Kumar, N., Zhang, W., Liu, L.: Dynamic virtual network embedding algorithm based on graph convolution neural network and reinforcement learning. IEEE Internet Things J. 9(12), 9389–9398 (2021)

    Article  Google Scholar 

  • Zhang, P., Li, Y., Kumar, N., Chen, N., Hsu, C.-H., Barnawi, A.: Distributed deep reinforcement learning assisted resource allocation algorithm for space-air-ground integrated networks. IEEE Trans. Netw. Serv. Manage. 20(3), 3348–3358 (2023). https://doi.org/10.1109/TNSM.2022.3232414

    Article  Google Scholar 

  • Zhang, P., Chen, N., Li, S., Choo, K.-K.R., Jiang, C., Wu, S.: Multi-domain virtual network embedding algorithm based on horizontal federated learning. IEEE Trans. Inf. Forensics Secur. 18, 3363–3375 (2023). https://doi.org/10.1109/TIFS.2023.3279587

    Article  Google Scholar 

  • Zhang, P., Chen, N., Xu, G., Kumar, N., Barnawi, A., Guizani, M., Duan, Y., Yu, K.: Multi-target-aware dynamic resource scheduling for cloud-fog-edge multi-tier computing network. IEEE Trans. Intell. Transp. Syst. (2023). https://doi.org/10.1109/TITS.2023.3330419

    Article  Google Scholar 

  • Zhang, P., Chen, N., Shen, S., Yu, S., Kumar, N., Hsu, C.-H.: AI-enabled space-air-ground integrated networks: management and optimization. IEEE Netw. 38(2), 186–192 (2024). https://doi.org/10.1109/MNET.131.2200477

    Article  Google Scholar 

  • Zhang, P., Chen, N., Kumar, N., Abualigah, L., Guizani, M., Duan, Y., Wang, J., Wu, S.: Energy allocation for vehicle-to-grid settings: a low-cost proposal combining DRL and VNE. IEEE Trans. Sustain. Comput. 9(1), 75–87 (2024). https://doi.org/10.1109/TSUSC.2023.3307551

    Article  Google Scholar 

  • Zhao, D., Lu, Y., Li, X., Li, Z., Liu, Y.: Cross-domain service function chain routing: multiagent reinforcement learning approaches. IEEE Trans. Circuits Syst. II Express Briefs 69(12), 4754–4758 (2022). https://doi.org/10.1109/TCSII.2022.3183345

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the Natural Science Foundation of Shandong Province under Grant ZR20231ZH017 (for administration), ZR2022LZH015 (for experimental data collection), and 2023QF025 (for experimental data collection), partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences) under Grant 2023ZD010 (for comparative experiment), partially supported by the National Natural Science Foundation of China under Grant 62173345 (for checking metrics), partially supported by the open project of the Key Laboratory of All Optical Network and Advanced Telecommunication Network of EMC, Ministry of Education, Beijing Jiaotong University under Grant AON2023K01 (for problem investigation), partially supported by the Integrated Innovation of Science, Education and Industry of Qilu University of Technology (Shandong Academy of Sciences) under Grant 2023PX057 (for comparative experiment), partially supported by the Talent Project of Qilu University of Technology (Shandong Academy of Sciences) under Grant 2023RCKY141 (for model development), partially supported by the RSF project under Grant 22-71-10095 (for checking metrics), partially supported by the Fundamental Research Funds for the Central Universities under Grant 2023JBZY014 (for problem investigation).

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Authors and Affiliations

  1. Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, Jinan, 250013, Shandong, China

    Lizhuang Tan & Peiying Zhang

  2. Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, 28789 Jingshi East Road, Jinan, 250013, Shandong, China

    Lizhuang Tan

  3. College of Computing and Information Technology, Shaqra University, Al Duwadimi Road, Shaqra, 15273, Riyadh, Saudi Arabia

    Amjad Aldweesh

  4. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, 10 Xitucheng Road, Beijing, 100876, China

    Ning Chen

  5. College of Science, China University of Petroleum (East China), 66 Changjiang West Road, Qingdao, 266580, Shandong, China

    Jian Wang

  6. Institute of Lightwave Technology, Beijing Jiaotong University, 3 Shangyuan Village, Beijing, 100044, China

    Jianyong Zhang

  7. Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), 66 Changjiang West Road, Qingdao, 266580, Shandong, China

    Peiying Zhang

  8. Department of Physics of Nanoscale Systems, South Ural State University, 76 Lenin Avenue, Chelyabinsk, 454080, Russian Federation

    Konstantin Igorevich Kostromitin

  9. School of Information and Communication Engineering, University of Electronic Science and Technology of China, 2006 West Avenue Road, Chengdu, 611731, Sichuan, China

    Yi Zhang

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  1. Lizhuang Tan
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  4. Jian Wang
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  8. Peiying Zhang
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Contributions

Lizhuang Tan: Conceptualization, Methodology, Writing - review & editing, Funding acquisition. Amjad Aldweesh: Conceptualization, Investigation, Methodology. Ning Chen: Conceptualization, Investigation, Methodology, Validation, Writing - original draft, Writing - review & editing. Jian Wang: Conceptualization, Writing - review & editing, Funding acquisition. Jianyong Zhang: Conceptualization, Writing - review & editing. Yi Zhang: Conceptualization, Writing - review & editing. Konstantin Igorevich Kostromitin: Investigation, Methodology. Peiying Zhang: Conceptualization, Funding acquisition. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Ning Chen or Jianyong Zhang.

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Tan, L., Aldweesh, A., Chen, N. et al. Energy efficient resource allocation based on virtual network embedding for IoT data generation. Autom Softw Eng 31, 66 (2024). https://doi.org/10.1007/s10515-024-00463-8

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