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LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing

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Abstract

Several applications have emerged with the proliferation of mobile devices to provide communication, learning, social networking, entertainment, and community computing services. Such applications include augmented reality, online gaming, and other real-time applications that need higher computational resources. These applications, executing on mobile devices, often need to access external computing resources and offload the application tasks to the cloud or mobile edge computing (MEC) servers. However, delivering task offloading results to the users in the MEC environment is a challenge, certainly when user mobility is high. Sub-optimal server selection at the offloading stage increases latency, energy consumption and deteriorates both quality of experience and quality of service. Existing techniques proposed in the literature handle computation offloading and mobility management separately. Without considering the real-time mobility factors, the solutions produced are sub-optimal. Some solutions exist to manage mobility, but they involve higher time complexity. We consider the user mobility in offloading decisions and present a lightweight mobility prediction and offloading (LiMPO) framework that offloads the compute-intensive tasks to the predicted user location using artificial neural networks with less complexity. In addition, we propose a multi-objective genetic algorithm based server selection technique that jointly optimizes latency and energy consumption while improving the resource utilization of MEC servers. The performance of the proposed framework is compared with two other techniques task-assignment with optimized mobility and dynamic mobility-aware offloading algorithm for edge computing. The simulation results show that LiMPO outperforms the others by latency reduction, energy efficiency, and enhanced resource utilization.

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References

  1. Wang, J., Lv, T., Huang, P., Mathiopoulos, P.T.: Mobility-aware partial computation offloading in vehicular networks: a deep reinforcement learning based scheme. China Commun. 17, 31–49 (2020)

    Google Scholar 

  2. Rahbari, D., Nickray, M.: Task offloading in mobile fog computing by classification and regression tree. Peer-to-Peer Netw. Appl. 13, 104–122 (2020)

    Article  Google Scholar 

  3. Dong, Q., Sinangil, M.E., Erbagci, B., Sun, D., Khwa, W.-S., Liao, H.-J., et al.: 15.3 A 351TOPS/W and 372.4 GOPS compute-in-memory SRAM macro in 7nm FinFET CMOS for machine-learning applications. In: 2020 IEEE International Solid-State Circuits Conference-(ISSCC), pp. 242–244 (2020)

  4. Dhaou, I.B., Tenhunen, H.: Design techniques of 5G mobile devices in the dark silicon era. In: Xiang, W., Zheng, K. (eds.) 5G Mobile Communications, pp. 381–400. Springer, Berlin (2017)

    Chapter  Google Scholar 

  5. Shuja, J., Bilal, K., Alasmary, W., Sinky, H., Alanazi, E.: Applying machine learning techniques for caching in next-generation edge networks: a comprehensive survey. J. Netw. Comput. Appl. 181, 103005 (2021)

    Article  Google Scholar 

  6. Babar, M., Khan, M.S., Ali, F., Imran, M., Shoaib, M.: Cloudlet computing: recent advances, taxonomy, and challenges. IEEE Access 9, 29609–29622 (2021)

    Article  Google Scholar 

  7. Malik, S.U., Akram, H., Gill, S.S., Pervaiz, H., Malik, H.: EFFORT: energy efficient framework for offload communication in mobile cloud computing. Softw. Pract. Exp. 51, 1896 (2020)

    Article  Google Scholar 

  8. Alfakih, T., Hassan, M.M., Gumaei, A., Savaglio, C., Fortino, G.: Task offloading and resource allocation for mobile edge computing by deep reinforcement learning based on SARSA. IEEE Access 8, 54074–54084 (2020)

    Article  Google Scholar 

  9. Duc, T.L., Leiva, R.G., Casari, P., Östberg, P.-O.: Machine learning methods for reliable resource provisioning in edge-cloud computing: a survey. ACM Comput. Surv. (CSUR) 52, 1–39 (2019)

    Article  Google Scholar 

  10. Mach, P., Becvar, Z.: Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun. Surv. Tutor. 19, 1628–1656 (2017)

    Article  Google Scholar 

  11. Peng, Q., Xia, Y., Feng, Z., Lee, J., Wu, C., Luo, X., et al.: Mobility-aware and migration-enabled online edge user allocation in mobile edge computing. In: 2019 IEEE International Conference on Web Services (ICWS), pp. 91-98 (2019)

  12. Yu, F., Chen, H., Xu, J.: DMPO: dynamic mobility-aware partial offloading in mobile edge computing. Future Gener. Comput. Syst. 89, 722–735 (2018)

    Article  Google Scholar 

  13. Wang, Z., Zhao, Z., Min, G., Huang, X., Ni, Q., Wang, R.: User mobility aware task assignment for mobile edge computing. Future Gener. Comput. Syst. 85, 1–8 (2018)

    Article  Google Scholar 

  14. Mustafa, E., Shuja, J., Jehangiri, A.I., Din, S., Rehman, F., Mustafa, S., et al.: Joint wireless power transfer and task offloading in mobile edge computing: a survey. Cluster Comput. (2021). https://doi.org/10.1007/s10586-021-03376-3

    Article  Google Scholar 

  15. Shakarami, A., Ghobaei-Arani, M., Shahidinejad, A.: A survey on the computation offloading approaches in mobile edge computing: a machine learning-based perspective. Comput. Netw. 182, 107496 (2020)

    Article  Google Scholar 

  16. Puliafito, C., Gonçalves, D.M., Lopes, M.M., Martins, L.L., Madeira, E., Mingozzi, E., et al.: MobFogSim: simulation of mobility and migration for fog computing. Simul. Modell. Pract. Theory 101, 102062 (2020)

    Article  Google Scholar 

  17. Luong, N.C., Hoang, D.T., Gong, S., Niyato, D., Wang, P., Liang, Y.-C., et al.: Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun. Surv. Tutor. 21, 3133–3174 (2019)

    Article  Google Scholar 

  18. Liu, C., Tang, F., Hu, Y., Li, K., Tang, Z., Li, K.: Distributed task migration optimization in MEC by extending multi-agent deep reinforcement learning approach. IEEE Trans. Parallel Distrib. Syst. 32, 1603–1614 (2020)

    Article  Google Scholar 

  19. uz Zaman, S.K., Jehangiri, A.I., Maqsood, T., Ahmad, Z., Umar, A.I., Shuja, J., et al.: Mobility-aware computational offloading in mobile edge networks: a survey. Cluster Comput. 4, 1–22 (2021)

    Google Scholar 

  20. Chamola, V., Tham, C.-K., Chalapathi, G.S.: Latency aware mobile task assignment and load balancing for edge cloudlets. In: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 587–592 (2017)

  21. Alam, M.G.R., Tun, Y.K., Hong, C.S.: Multi-agent and reinforcement learning based code offloading in mobile fog. In: International Conference on Information Networking (ICOIN), pp. 285–290 (2016)

  22. Wang, S., Urgaonkar, R., Zafer, M., He, T., Chan, K., Leung, K.K.: Dynamic service migration in mobile edge computing based on Markov decision process. IEEE/ACM Trans. Netw. 27, 1272–1288 (2019)

    Article  Google Scholar 

  23. Xia, X., Zhou, Y., Li, J., Yu, R.: Quality-aware sparse data collection in MEC-enhanced mobile crowdsensing systems. IEEE Trans. Comput. Soc. Syst. 6, 1051–1062 (2019)

    Article  Google Scholar 

  24. Deng, S., Huang, L., Taheri, J., Zomaya, A.Y.: Computation offloading for service workflow in mobile cloud computing. IEEE Trans. Parallel Distrib. Syst. 26, 3317–3329 (2015)

    Article  Google Scholar 

  25. Waqas, M., Niu, Y., Li, Y., Ahmed, M., Jin, D., Chen, S., et al.: Mobility-aware device-to-device communications: principles, practice and challenges. In: IEEE Communications Surveys Tutorials, June 2019

  26. Lai, P., He, Q., Abdelrazek, M., Chen, F., Hosking, J., Grundy, J., et al.: Optimal edge user allocation in edge computing with variable sized vector bin packing. In: International Conference on Service-Oriented Computing, pp. 230–245 (2018)

  27. Xu, J., Li, X., Liu, X., Zhang, C., Fan, L., Gong, L., et al.: Mobility-aware workflow offloading and scheduling strategy for mobile edge computing. In: International Conference on Algorithms and Architectures for Parallel Processing, pp. 184–199 (2019)

  28. Wu, C.-L., Chiu, T.-C., Wang, C.-Y., Pang, A.-C.: Mobility-aware deep reinforcement learning with glimpse mobility prediction in edge computing. In: ICC 2020–2020 IEEE International Conference on Communications (ICC), pp. 1–7 (2020)

  29. Zhao, X., Shi, Y., Chen, S.: MAESP: mobility aware edge service placement in mobile edge networks. Comput. Netw. 182, 107435 (2020)

    Article  Google Scholar 

  30. Sun, Y., Zhou, S., Xu, J.: EMM: energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE J. Sel. Areas Commun. 35, 2637–2646 (2017)

    Article  Google Scholar 

  31. Wu, C., Peng, Q., Xia, Y., Lee, J.: Mobility-aware tasks offloading in mobile edge computing environment. In: 2019 Seventh International Symposium on Computing and Networking (CANDAR), pp. 204–210 (2019)

  32. Thananjeyan, S., Chan, C.A., Wong, E., Nirmalathas, A.: Mobility-aware energy optimization in hosts selection for computation offloading in multi-access edge computing. In: IEEE Open Journal of the Communications Society (2020)

  33. Ghosh, S., Mukherjee, A., Ghosh, S.K., Buyya, R.: Mobi-IoST: mobility-aware cloud-fog-edge-iot collaborative framework for time-critical applications. In: IEEE Transactions on Network Science and Engineering (2019)

  34. Wang, C., Elliott, R.C., Feng, D., Krzymien, W.A., Zhang, S., Melzer, J.: A framework for MEC-enhanced small-cell HetNet with massive MIMO. IEEE Wirel. Commun. 27, 64–72 (2020)

    Article  Google Scholar 

  35. Khan, A.U.R., uz Zaman, S.K., Malik, S.U.R., Khan, A.N., Maqsood, T., Madani, S.A.: Formal verification and performance evaluation of task scheduling heuristics for Makespan optimization and workflow distribution in large-scale computing systems. Comput. Syst. Sci. Eng. 32, 227 (2017)

    Google Scholar 

  36. Xu, C., Zheng, G., Zhao, X.: Energy-minimization task offloading and resource allocation for mobile edge computing in NOMA heterogeneous networks. In: IEEE Transactions on Vehicular Technology (2020)

  37. De Maio, V., Brandic, I.: First hop mobile offloading of dag computations. In: 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 83–92 (2018)

  38. Wang, D., Tian, X., Cui, H., Liu, Z.: Reinforcement learning-based joint task offloading and migration schemes optimization in mobility-aware MEC network. China Commun. 17, 31–44 (2020)

    Google Scholar 

  39. Grochowski, E., Annavaram, M.: Energy per instruction trends in Intel microprocessors. Technology@ Intel Mag 4, 1–8 (2006)

    Google Scholar 

  40. Mishra, S.K., Puthal, D., Sahoo, B., Jena, S.K., Obaidat, M.S.: An adaptive task allocation technique for green cloud computing. J. Supercomput. 74, 370–385 (2018)

    Article  Google Scholar 

  41. Bai, T., Pan, C., Deng, Y., Elkashlan, M., Nallanathan, A., Hanzo, L.: Latency minimization for intelligent reflecting surface aided mobile edge computing. IEEE J. Sel. Areas Commun. 38, 2666–2682 (2020)

    Article  Google Scholar 

  42. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24, 2795–2808 (2015)

    Article  Google Scholar 

  43. Chen, M., Hao, Y.: Task offloading for mobile edge computing in software defined ultra-dense network. IEEE J. Sel. Areas Commun. 36, 587–597 (2018)

    Article  Google Scholar 

  44. uz Zaman, S.K., Tahir Maqsood, M.A., Bilal, K.: A load balanced task scheduling heuristic for large-scale computing systems. Comput. Syst. Sci. Eng. 34, 79 (2019)

    Article  Google Scholar 

  45. Rădulescu, C.Z., Rădulescu, D.M.: A performance and power consumption analysis based on processor power models. In: 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), pp. 1–4 (2020)

  46. Daraghmeh, M., Al Ridhawi, I., Aloqaily, M., Jararweh, Y., Agarwal, A.: A power management approach to reduce energy consumption for edge computing servers. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), pp. 259–264 (2019)

  47. Cao, B., Zhang, L., Li, Y., Feng, D., Cao, W.: Intelligent offloading in multi-access edge computing: a state-of-the-art review and framework. IEEE Commun. Mag. 57, 56–62 (2019)

    Article  Google Scholar 

  48. Duong, T.M., Kwon, S.: Vertical handover analysis for randomly deployed small cells in heterogeneous networks. IEEE Trans. Wirel. Commun. 19, 2282–2292 (2020)

    Article  Google Scholar 

  49. Liu, X., Yu, J., Qi, H., Yang, J., Rong, W., Zhang, X., et al.: Learning to predict the mobility of users in mobile mmWave networks. IEEE Wirel. Commun. 27, 124–131 (2020)

    Article  Google Scholar 

  50. Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 3–24 (2007)

    Google Scholar 

  51. Shakarami, A., Shahidinejad, A., Ghobaei-Arani, M.: An autonomous computation offloading strategy in mobile edge computing: a deep learning-based hybrid approach. J. Netw. Comput. Appl. 178, 102974 (2021)

    Article  Google Scholar 

  52. E. Access: Further advancements for E-UTRA physical layer aspects. 3GPP Tech. Specif. TR 36, V2 (2010)

    Google Scholar 

  53. Wu, W., Zhou, F., Hu, R.Q., Wang, B.: Energy-efficient resource allocation for secure NOMA-enabled mobile edge computing networks. IEEE Trans. Commun. 68, 493–505 (2020)

    Article  Google Scholar 

  54. Codeca, L., Frank, R., Engel, T.: Luxembourg sumo traffic (lust) scenario: 24 hours of mobility for vehicular networking research. In: 2015 IEEE Vehicular Networking Conference (VNC), pp. 1–8 (2015)

  55. Piri, E., Ruuska, P., Kanstrén, T., Mäkelä, J., Korva, J., Hekkala, A. et al.: 5GTN: A test network for 5G application development and testing. In: 2016 European Conference on Networks and Communications (EuCNC), pp. 313–318 (2016)

  56. Lema, M.A., Laya, A., Mahmoodi, T., Cuevas, M., Sachs, J., Markendahl, J., et al.: Business case and technology analysis for 5G low latency applications. IEEE Access 5, 5917–5935 (2017)

    Google Scholar 

  57. uz Zaman, S.K., Maqsood, T., Ali, M., Bilal, K., Madani, S.A., Khan, A.U.R.: A load balanced task scheduling heuristic for large-scale computing systems. Comput. Syst. Sci. Eng 34, 1–12 (2019)

    Google Scholar 

  58. Hossain, M.K., Rahman, M., Hossain, A., Rahman, S.Y., Islam, M.M.: Active & idle virtual machine migration algorithm-a new ant colony optimization approach to consolidate virtual machines and ensure green cloud computing

  59. Bengio, Y.: Practical recommendations for gradient-based training of deep architectures. In: Montavon, G., Orr, G.B. (eds.) Neural Networks: Tricks of the Trade, pp. 437–478. Springer, Berlin (2012)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, Hazara University Mansehra, Mansehra, Pakistan

    Sardar Khaliq uz Zaman, Ali Imran Jehangiri, Arif Iqbal Umar & Zulfiqar Ahmad

  2. Department of Computer Science, COMSATS University Islamabad (CUI), Abbottabad Campus, Abbottabad, Pakistan

    Tahir Maqsood, Nuhman ul Haq & Junaid Shuja

  3. School of Engineering, Computing and Informatics, Dar Al-Hekma University, Jeddah, Saudi Arabia

    Imed Ben Dhaou

  4. Department of Computing, University of Turku, Turku, Finland

    Imed Ben Dhaou

  5. Department of Technology, Higher Institute of Computer Sciences and Mathematics, University of Monastir, Monastir, Tunisia

    Imed Ben Dhaou

  6. Department of Electrical Engineering, Unaizah College of Engineering, Qassim University, Buraydah, Saudi Arabia

    Mohammed F. Alsharekh

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  1. Sardar Khaliq uz Zaman
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Zaman, S.K.u., Jehangiri, A.I., Maqsood, T. et al. LiMPO: lightweight mobility prediction and offloading framework using machine learning for mobile edge computing. Cluster Comput 26, 99–117 (2023). https://doi.org/10.1007/s10586-021-03518-7

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