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2022 | OriginalPaper | Buchkapitel

2. Dynamic Computation Offloading for Energy Efficiency in Mobile Edge Computing

verfasst von : Ying Chen, Ning Zhang, Yuan Wu, Sherman Shen

Erschienen in: Energy Efficient Computation Offloading in Mobile Edge Computing

Verlag: Springer International Publishing

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Abstract

With the development of Internet of Things (IoT) and 5G techniques, the number of computation-intensive and delay-sensitive applications is increasing rapidly. However, the computing capacity of IoT devices is limited and the devices can not process so much data by themselves, which increases the delay and lead to the decline of service quality. Mobile edge computing is a promising computing paradigm, which deploys servers near IoT devices to provide services. However, offloading tasks to MEC for processing consumes a lot of energy. In order to improve the service quality of IoT devices and reduce energy consumption at the same time, the stochastic optimization problem of task offloading in the MEC system is studied. The goal of this energy efficient task offloading problem is to minimize energy consumption while minimizing the length of task queue on IoT devices. However, there are sever challenges faced with this problem. For example, the uncertainty of wireless channel states and the dynamics of task arrivals make it very complex to solve this problem. In this chapter, we take advantage of stochastic optimization techniques to solve this problem, and propose the distributed EEDCO scheme. The theoretical performance analysis for the EEDCO scheme is provided, and experiments are also conducted to verify the EEDCO scheme.

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Vorheriges Kapitel Introduction

Nächstes Kapitel Energy Efficient Offloading and Frequency Scaling for Internet of Things Devices

D. Chen et al., S2M: A lightweight acoustic fingerprints-based wireless device authentication protocol. IEEE Internet Things J. 4(1), 88–100, Feb. 2017. https://doi.org/10.1109/JIOT.2016.2619679CrossRef

H. Duan, Y. Zheng, C. Wang, X. Yuan, Treasure collection on foggy islands: building secure network archives for Internet of Things. IEEE Internet Things J. 6(2), 2637–2650 (2019). https://doi.org/10.1109/JIOT.2018.2872461CrossRef

L.F. Bittencourt, J. Diaz-Montes, R. Buyya, O.F. Rana, M. Parashar, Mobility-aware application scheduling in fog computing. IEEE Cloud Comput. 4(2), 26–35 (2017). https://doi.org/10.1109/MCC.2017.27CrossRef

W. Shi, J. Cao, Q. Zhang, Y. Li, L. Xu, Edge computing: vision and challenges. IEEE Internet Things J. 3(5), 637–646 (2016). https://doi.org/10.1109/JIOT.2016.2579198CrossRef

X. Chen, Q. Shi, L. Yang, J. Xu, ThriftyEdge: resource-efficient edge computing for intelligent IoT applications. IEEE Netw. 32(1), 61–65 (2018). https://doi.org/10.1109/MNET.2018.1700145CrossRef

A. Kapsalis, P. Kasnesis, I.S. Venieris, D.I. Kaklamani, C.Z. Patrikakis, A cooperative fog approach for effective workload balancing. IEEE Cloud Comput. 4(2), 36–45 (2017). https://doi.org/10.1109/MCC.2017.25CrossRef

H. Wu, Y. Sun, K. Wolter, Energy-efficient decision making for mobile cloud offloading. IEEE Trans. Cloud Comput. 8(2), 570–584 (2020). https://doi.org/10.1109/TCC.2018.2789446CrossRef

Y. Wu, J. Chen, L.P. Qian, J. Huang, X.S. Shen, Energy-aware cooperative traffic offloading via device-to-device cooperations: an analytical approach. IEEE Trans. Mob. Comput. 16(1), 97–114 (2017). https://doi.org/10.1109/TMC.2016.2539950CrossRef

F. Liu, P. Shu, J.C.S. Lui, AppATP: an energy conserving adaptive mobile-cloud transmission protocol. IEEE Trans. Comput. 64(11), 3051–3063 (2015). https://doi.org/10.1109/TC.2015.2401032MathSciNetCrossRef

10.

O. Muñoz, A. Pascual-Iserte, J. Vidal, Optimization of radio and computational resources for energy efficiency in latency-constrained application offloading. IEEE Trans. Veh. Technol. 64(10), 4738–4755 (2015). https://doi.org/10.1109/TVT.2014.2372852CrossRef

11.

M.R. Palattella et al., Internet of Things in the 5G era: enablers, architecture, and business models. IEEE J. Sel. Areas Commun. 34(3), 510–527 (2016). https://doi.org/10.1109/JSAC.2016.2525418CrossRef

12.

X. Lyu et al., Optimal schedule of mobile edge computing for internet of things using partial information. IEEE J. Sel. Areas Commun. 35(11), 2606–2615 (2017). https://doi.org/10.1109/JSAC.2017.2760186CrossRef

13.

Y. Mao, J. Zhang, S.H. Song, K.B. Letaief, Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Trans. Wirel. Commun. 16(9), 5994–6009 (2017). https://doi.org/10.1109/TWC.2017.2717986CrossRef

14.

K. Wang, K. Yang, C.S. Magurawalage, Joint energy minimization and resource allocation in C-RAN with mobile cloud. IEEE Trans. Cloud Comput. 6(3), 760–770 (2018). https://doi.org/10.1109/TCC.2016.2522439CrossRef

15.

X. Chen, L. Jiao, W. Li, X. Fu, Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2016). https://doi.org/10.1109/TNET.2015.2487344CrossRef

16.

Y. Yu, J. Zhang, K.B. Letaief, Joint subcarrier and CPU time allocation for mobile edge computing, in 2016 IEEE Global Communications Conference (GLOBECOM) (2016), pp. 1–6. https://doi.org/10.1109/GLOCOM.2016.7841937

17.

X. Lyu, H. Tian, C. Sengul, P. Zhang, Multiuser joint task offloading and resource optimization in proximate clouds. IEEE Trans. Veh. Technol. 66(4), 3435–3447 (2017). https://doi.org/10.1109/TVT.2016.2593486CrossRef

18.

C. You, K. Huang, H. Chae, Energy efficient mobile cloud computing powered by wireless energy transfer. IEEE J. Sel. Areas Commun. 34(5), 1757–1771 (2016). https://doi.org/10.1109/JSAC.2016.2545382CrossRef

19.

Y. Chen, Z. Liu, Y. Zhang, Y. Wu, X. Chen, L. Zhao, Deep reinforcement learning-based dynamic resource management for mobile edge computing in industrial Internet of Things. IEEE Trans. Ind. Inform. 17(7), 4925–4934 (2021). https://doi.org/10.1109/TII.2020.3028963CrossRef

20.

Y. Zhang, X. Chen, Y. Chen, Z. Li, J. Huang, Cost efficient scheduling for delay-sensitive tasks in edge computing system, in 2018 IEEE International Conference on Services Computing (SCC) (2018), pp. 73–80. https://doi.org/10.1109/SCC.2018.00017

21.

X. Lyu et al., Distributed online optimization of fog computing for selfish devices with out-of-date information. IEEE Trans. Wirel. Commun. 17(11), 7704–7717 (2018). https://doi.org/10.1109/TWC.2018.2869764CrossRef

22.

J. Kwak, Y. Kim, J. Lee, S. Chong, DREAM: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J. Sel. Areas Commun. 33(12), 2510–2523 (2015). https://doi.org/10.1109/JSAC.2015.2478718CrossRef

23.

Z. Jiang, S. Mao, Energy delay tradeoff in cloud offloading for multi-core mobile devices. IEEE Access 3, 2306–2316 (2015). https://doi.org/10.1109/ACCESS.2015.2499300CrossRef

24.

Y. Chen, Y. Zhang, Y. Wu, L. Qi, X. Chen, X. Shen, Joint task scheduling and energy management for heterogeneous mobile edge computing with hybrid energy supply. IEEE Internet Things J. 7(9), 8419–8429 (2020). https://doi.org/10.1109/JIOT.2020.2992522CrossRef

25.

Y. Chen, C. Lin, J. Huang, X. Xiang, X. Shen, Energy efficient scheduling and management for large-scale services computing systems. IEEE Trans. Serv. Comput. 10(2), 217–230 (2017). https://doi.org/10.1109/TSC.2015.2444845CrossRef

26.

X. Chen, Y. Zhang, Y. Chen, Cost-efficient request scheduling and resource provisioning in multiclouds for Internet of Things. IEEE Internet Things J. 7(3), 1594–1602 (2020). https://doi.org/10.1109/JIOT.2019.2948432CrossRef

27.

Y. Chen, N. Zhang, Y. Zhang, X. Chen, Dynamic computation offloading in edge computing for Internet of Things. IEEE Internet Things J. 6(3), 4242–4251 (2019). https://doi.org/10.1109/JIOT.2018.2875715CrossRef

28.

Y. Chen, J. Huang, X. Xiang, C. Lin, Energy efficient dynamic service selection for large-scale web service systems, in 2014 IEEE International Conference on Web Services (2014), pp. 558–565. https://doi.org/10.1109/ICWS.2014.84

29.

Y. Chen, J. Huang, C. Lin, J. Hu, A partial selection methodology for efficient QoS-aware service composition. IEEE Trans. Serv. Comput. 8(3), 384–397 (2015). https://doi.org/10.1109/TSC.2014.2381493CrossRef

30.

G. Chen, J. Huang, B. Cheng, J. Chen, A social network based approach for IoT device management and service composition, in 2015 IEEE World Congress on Services (2015), pp. 1–8. https://doi.org/10.1109/SERVICES.2015.9

31.

X. Chen, Z. Liu, Y. Chen, Z. Li, Mobile edge computing based task offloading and resource allocation in 5G ultra-dense networks. IEEE Access 7, 184172–184182 (2019). https://doi.org/10.1109/ACCESS.2019.2960547CrossRef

32.

S. Chen, X. Chen, Y. Chen, Z. Li, Distributed computation offloading based on stochastic game in multi-server mobile edge computing networks, in 2019 IEEE International Conference on Smart Internet of Things (SmartIoT) (2019), pp. 77–84. https://doi.org/10.1109/SmartIoT.2019.00021

33.

P. Yao, X. Chen, Y. Chen, Z. Li, Deep reinforcement learning based offloading scheme for mobile edge computing, in 2019 IEEE International Conference on Smart Internet of Things (SmartIoT) (2019), pp. 417–421. https://doi.org/10.1109/SmartIoT.2019.00074

34.

Y. Yang, X. Chen, Y. Chen, Z. Li, Green-oriented offloading and resource allocation by reinforcement learning in MEC, in 2019 IEEE International Conference on Smart Internet of Things (SmartIoT) (2019), pp. 378–382. https://doi.org/10.1109/SmartIoT.2019.00066

35.

F. Zhao, Y. Chen, Y. Zhang, Z. Liu, X. Chen, Dynamic offloading and resource scheduling for mobile-edge computing with energy harvesting devices. IEEE Trans. Netw. Serv. Manag. 18(2), 2154–2165 (2021). https://doi.org/10.1109/TNSM.2021.3069993CrossRef

36.

37.

J. Huang, C. Lin, Agent-based green web service selection and dynamic speed scaling, in 2013 IEEE 20th International Conference on Web Services (2013), pp. 91–98. https://doi.org/10.1109/ICWS.2013.22

38.

39.

S. Li, J. Huang, Energy efficient resource management and task scheduling for IoT services in edge computing paradigm, in 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC) (2017), pp. 846–851. https://doi.org/10.1109/ISPA/IUCC.2017.00129

40.

M. Guo, W. Wang, X. Huang, Y. Chen, L. Zhang, L. Chen, Lyapunov-based partial computation offloading for multiple mobile devices enabled by harvested energy in MEC. IEEE Internet Things J.. https://doi.org/10.1109/JIOT.2021.3118016

41.

J. Zhang et al., Stochastic computation offloading and trajectory scheduling for UAV-assisted mobile edge computing. IEEE Internet Things J. 6(2), 3688–3699 (2019). https://doi.org/10.1109/JIOT.2018.2890133CrossRef

42.

P. Lai et al., Dynamic user allocation in stochastic mobile edge computing systems. IEEE Trans. Serv. Comput. (2021). https://doi.org/10.1109/TSC.2021.3063148

43.

Y. Li, S. Xia, M. Zheng, B. Cao, Q. Liu, Lyapunov optimization based trade-off policy for mobile cloud offloading in heterogeneous wireless networks. IEEE Trans. Cloud Comput. (2019). https://doi.org/10.1109/TCC.2019.2938504

44.

A. Bonadio, F. Chiti, R. Fantacci, Performance analysis of an edge computing SaaS system for mobile users. IEEE Trans. Veh. Technol. 69(2), 2049–2057 (2020). https://doi.org/10.1109/TVT.2019.2957938CrossRef

45.

Y. Zhang, B. Feng, W. Quan, G. Li, H. Zhou, H. Zhang, Theoretical analysis on edge computation offloading policies for IoT devices. IEEE Internet Things J. 6(3), 4228–4241 (2019). https://doi.org/10.1109/JIOT.2018.2875599CrossRef

46.

R. Duan, J. Wang, J. Du, C. Jiang, T. Bai, Y. Ren, Power-delay trade-off for heterogenous cloud enabled multi-UAV systems, in ICC 2019 - 2019 IEEE International Conference on Communications (ICC) (2019), pp. 1–6. https://doi.org/10.1109/ICC.2019.8761377

47.

L. Liu, X. Qin, Z. Zhang, P. Zhang, Joint task offloading and resource allocation for obtaining fresh status updates in multi-device MEC systems. IEEE Access 8, 38248–38261 (2020). https://doi.org/10.1109/ACCESS.2020.2976048CrossRef

48.

R. Fantacci, B. Picano, Performance analysis of a delay constrained data offloading scheme in an integrated cloud-fog-edge computing system. IEEE Trans. Veh. Technol. 69(10), 12004–12014 (2020). https://doi.org/10.1109/TVT.2020.3008926CrossRef

49.

R. Lin et al., Distributed optimization for computation offloading in edge computing. IEEE Trans. Wirel. Commun. 19(12), 8179–8194 (2020). https://doi.org/10.1109/TWC.2020.3019805CrossRef

50.

H. Wu, K. Wolter, P. Jiao, Y. Deng, Y. Zhao, M. Xu, EEDTO: an energy-efficient dynamic task offloading algorithm for blockchain-enabled IoT-edge-cloud orchestrated computing. IEEE Internet Things J. 8(4), 2163–2176 (2021). https://doi.org/10.1109/JIOT.2020.3033521CrossRef

51.

D. Zhang et al., Near-optimal and truthful online auction for computation offloading in green edge-computing systems. IEEE Trans. Mob. Comput. 19(4), 880–893 (2020). https://doi.org/10.1109/TMC.2019.2901474CrossRef

Titel: Dynamic Computation Offloading for Energy Efficiency in Mobile Edge Computing
verfasst von: Ying Chen
Ning Zhang
Yuan Wu
Sherman Shen
Verlag: Springer International Publishing
Buch: Energy Efficient Computation Offloading in Mobile Edge Computing
Print ISBN: 978-3-031-16821-5

Electronic ISBN: 978-3-031-16822-2

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-16822-2_2

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