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Partial offloading strategy for mobile edge computing considering mixed overhead of time and energy

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Abstract

Mobile edge computing (MEC) utilizes wireless access network to provide powerful computing resources for mobile users to improve the user experience, which mainly includes two aspects: time and energy consumption. Time refers to the latency consumed to process user tasks, while energy consumption refers to the total energy consumed in processing tasks. In this paper, the time and energy consumption in user experience are weighted as a mixed overhead and then optimized jointly. We formulate a mixed overhead of time and energy (MOTE) minimization problem, which is a nonlinear programming problem. In order to solve this problem, the block coordinate descent method to deal with each variable step by step is adopted. We further analyze the minimum value of delay parameters in the model, and examine two special cases: 1-offloading and 0-offloading. In 1-offloading, all the task data is offloaded to MEC server, and no data offloaded in 0-offloading. The necessary and sufficient conditions for the existence of two special cases are also deduced. Besides, the multi-user situation is also discussed. In the performance evaluation, we compare MOTE with other offloading schemes, such as exhaustive strategy and Monte Carlo simulation method-based strategy to evaluate the optimality. The simulation results show that MOTE always achieves the minimal overhead compared to other algorithms.

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References

  1. Panwar N, Sharma S, Singh AK (2016) A survey on 5G: the next generation of mobile communication. Phys Commun 18:64–84

    Article  Google Scholar 

  2. Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong AC, Zhang JC (2014) What will 5G be? IEEE J Sel Areas Commun 32(6):1065–1082

    Article  Google Scholar 

  3. Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surv Tutor 19(4):2322–2358

    Article  Google Scholar 

  4. Mach P, Becvar Z (2017) Mobile edge computing: a survey on architecture and computation offloading. IEEE Commun Surv Tutor 19(3):1628–1656

    Article  Google Scholar 

  5. Han G, Yang X, Liu L, Zhang W (2018) A joint energy replenishment and data collection algorithm in wireless rechargeable sensor networks. IEEE Internet Thing J 5(4):2596–2604

    Article  Google Scholar 

  6. Han G, Liu L, Zhang W, Chan S (2018) A hierarchical jammed-area mapping service for ubiquitous communication in smart communities. IEEE Commun Mag 56(1):92–98

    Article  Google Scholar 

  7. Han G, Wang H, Jiang J, Zhang W, Chan S (2018) CASLP: a confused arc-based source location privacy protection scheme in WSNs for IoT. IEEE Commun Mag 56(9):42–47

    Article  Google Scholar 

  8. Han G, Guan H, Wu J, Chan S, Shu L, Zhang W (2018) An uneven cluster-based mobile charging algorithm for wireless rechargeable sensor networks. IEEE Syst J. https://doi.org/10.1109/JSYST.2018.2879084

    Article  Google Scholar 

  9. He S, Xie K, Chen W, Zhang D, Wen J (2018) Energy-aware routing for SWIPT in multi-hop energy-constrained wireless network. IEEE Access 6:17996–18008

    Article  Google Scholar 

  10. Cao D, Liu Y, Ma X, Wang J, Ji B, Feng C, Si J (2019) A relay-node selection on curve road in vehicular networks. IEEE Access 7:12714–12728

    Article  Google Scholar 

  11. Cao D, Zheng B, Wang J, Ji B, Feng C (2018) Design and analysis of a general relay-node selection mechanism on intersection in vehicular networks. Sensors 18:4251. https://doi.org/10.3390/s18124251

    Article  Google Scholar 

  12. Li W, Chen Z, Gao X, Liu W, Wang J (2018) Multi-model framework for indoor localization under mobile edge computing environment. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2018.2872133

    Article  Google Scholar 

  13. Tang Q, Xie M, Yang K, Luo Y, Zhou D, Song Y (2018) A decision function based smart charging and discharging strategy for electric vehicle in smart grid. Mob Netw Appl. https://doi.org/10.1007/s11036-018-1049-4

    Article  Google Scholar 

  14. Tang Q, Wang K, Luo Y, Yang K (2017) Congestion balanced green charging networks for electric vehicles in smart grid. In: Proceedings of IEEE global communications conference, pp 1–6

  15. Tang Q, Yang K, Zhou D, Luo Y, Yu F (2016) A real-time dynamic pricing algorithm for smart grid with unstable energy providers and malicious users. IEEE Internet Things J 3(4):554–562

    Article  Google Scholar 

  16. Wang S, Zhang X, Zhang Y, Wang L, Yang J, Wang W (2017) A survey on mobile edge networks: convergence of computing, caching and communications. IEEE Access 5:6757–6779

    Article  Google Scholar 

  17. Wang X, Wang K, Wu S, Di S, Jin H, Yang K, Ou S (2018) Dynamic resource scheduling in mobile edge cloud with cloud radio access network. IEEE Trans Parallel Distrib Syst 29(11):2429–2445

    Article  Google Scholar 

  18. Mei H, Wang K, Yang K (2017) Multi-layer cloud-RAN with cooperative resource allocations for low-latency computing and communication services. IEEE Access 5:19023–19032

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. Wu H, Wang Q, Wolter K (2013) Tradeoff between performance improvement and energy saving in mobile cloud offloading systems. In: Proceedings of IEEE international conference on communications workshops (ICC), pp 728–732

  21. You C, Huang K, Chae H (2016) Energy efficient mobile cloud computing powered by wireless energy transfer. IEEE J Sel Areas Commun 34(5):1757–1771

    Article  Google Scholar 

  22. Barbarossa S, Sardellitti S, Di Lorenzo P (2014) Communicating while computing: distributed mobile cloud computing over 5G heterogeneous networks. IEEE Signal Process Mag 31(6):45–55

    Article  Google Scholar 

  23. Wang K, Yang K, Magurawalage CS (2018) Joint energy minimization and resource allocation in C-RAN with mobile cloud. IEEE Trans Cloud Comput 6(3):760–770

    Article  Google Scholar 

  24. Sun H, Zhou F, Hu RQ (2019) Joint offloading and computation energy efficiency maximization in a mobile edge computing system. IEEE Trans Veh Technol 68(3):3052–3056

    Google Scholar 

  25. Hao Y, Chen M, Hu L, Hossain MS, Ghoneim A (2018) Energy efficient task caching and offloading for mobile edge computing. IEEE Access 6:11365–11373

    Article  Google Scholar 

  26. Ren J, Yu G, Cai Y, He Y, Qu F (2017) Partial offloading for latency minimization in mobile-edge computing. In: Proceedings of IEEE global communications conference (GLOBECOM), pp 1–6

  27. Cao X, Wang F, Xu J, Zhang R, Cui S (2018) Joint computation and communication cooperation for mobile edge computing. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2018.2875246

    Article  Google Scholar 

  28. Jia M, Cao J, Yang L (2014) Heuristic offloading of concurrent tasks for computation-intensive applications in mobile cloud computing. In: Proceedings of IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 352–357

  29. Liu J, Mao Y, Zhang J, Letaief KB (2016) Delay-optimal computation task scheduling for mobile-edge computing systems. In: Proceedings of IEEE international symposium on information theory (ISIT), pp 1451–1455

  30. Mahmoodi SE, Uma RN, Subbalakshmi KP (2016) Optimal joint scheduling and cloud offloading for mobile applications. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2560808

    Article  Google Scholar 

  31. Mao S, Leng S, Yang K, Zhao Q, Liu M (2017) Energy efficiency and delay tradeoff in multi-user wireless powered mobile-edge computing systems. In: Proceedings of IEEE global communications conference (GLOBECOM), pp 1–6

  32. Mahmoodi SE, Subbalakshmi KP, Sagar V (2017) Cloud offloading for multi-radio enabled mobile devices. In: Proceedings of IEEE international conference on communications workshops (ICC), pp 5473–5478

  33. Zhang W, Wen Y, Wu DO (2015) Collaborative task execution in mobile cloud computing under a stochastic wireless channel. IEEE Trans Wirel Commun 14(1):81–93

    Article  Google Scholar 

  34. Wang Y, Sheng M, Wang X, Wang L, Li J (2016) Mobile-edge computing: partial computation offloading using dynamic voltage scaling. IEEE Trans Commun 64(10):4268–4282

    Google Scholar 

  35. Munoz O, Pascual-Iserte A, Vidal J (2015) Optimization of radio and computational resources for energy efficiency in latency-constrained application offloading. IEEE Trans Veh Technol 64(10):4738–4755

    Article  Google Scholar 

  36. Kao Y, Krishnamachari B, Ra M, Bai F (2017) Hermes: latency optimal task assignment for resource-constrained mobile computing. IEEE Trans Mob Comput 16(11):3056–3069

    Article  Google Scholar 

  37. Wang F, Xu J, Ding Z (2019) Multi-antenna NOMA for computation offloading in multiuser mobile edge computing systems. IEEE Trans Commun 67(3):2450–2463

    Article  Google Scholar 

  38. Chen X (2015) Decentralized computation offloading game for mobile cloud computing. IEEE Trans Parallel Distrib Syst 26(4):974–983

    Article  Google Scholar 

  39. Chen X, Jiao L, Li W, Fu X (2016) Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans Netw 24(5):2795–2808

    Article  Google Scholar 

  40. Cheng Z, Li P, Wang J, Guo S (2015) Just-in-time code offloading for wearable computing. IEEE Trans Emerg Top Comput 3(1):74–83

    Article  Google Scholar 

  41. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Key Research and Development Program, No. 2017YFE0125300 and the National Natural Science Foundation of China-Guangdong Joint Fund under Grant No. U1801264, the Jiangsu Key Research and Development Program, No. BE2019648, in part by the Open fund of State Key Laboratory of Acoustics under Grant SKLA201901, in part by the National Natural Science Foundation of China (Grant Nos. 61772087, 61303043), in part by the Outstanding Youth Project of Hunan Province Education Department (Grant No. 18B162), and in part by the “Double First-class” International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology (Grant No. 2018IC23).

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Qiang Tang, Haimei Lyu & Jin Wang

  2. School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China

    Guangjie Han

  3. Department of Information and Communication Systems, Hohai University, Changzhou, 213022, China

    Guangjie Han

  4. State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, Beijing, China

    Guangjie Han

  5. Department of Computer and Information Sciences, Northumbria University, Newcastle, UK

    Kezhi Wang

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  1. Qiang Tang
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  4. Jin Wang
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Correspondence to Guangjie Han.

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Tang, Q., Lyu, H., Han, G. et al. Partial offloading strategy for mobile edge computing considering mixed overhead of time and energy. Neural Comput & Applic 32, 15383–15397 (2020). https://doi.org/10.1007/s00521-019-04401-8

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  • DOI: https://doi.org/10.1007/s00521-019-04401-8

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