移动边缘计算的多用户计算卸载研究及实验

doi:10.19665/j.issn1001-2400.2020.04.011

Abstract

Abstract:

The fundamental problem of multi-user computation offloading for Mobile Edge Computing is investigated in heterogeneous overlay networks where each user can connect and offload its computing workloads to multiple heterogeneous wireless access points in parallel. The problem of average user overhead minimization with the delay constraint is formulated to obtain the optimal strategy of workload partition and heterogeneous resource allocation. A successive convex approximation (SCA) based algorithm is finally developed, which addresses the problem of non-convex optimization by iteratively solving a sequence of separable strongly convex problems. Numerical results are presented to prove that the proposed offloading mechanism can effectively reduce the service latency and energy consumption of users compared with the conventional non-cooperative approach.

Key words: mobile edge computing, heterogeneous networks, workload offloading

CLC Number:

TN915.41

LU Jixiang,FANG Boya. Research and experiment on multi-user computational offloading based on mobile edge computing[J].Journal of Xidian University, 2020, 47(4): 78-85.

Figures/Tables 5

References 22

[1]	ABBAS N, ZHANG Y, TAHERKORDI A, et al. Mobile Edge Computing: a Survey[J]. IEEE Internet of Things Journal, 2018,5(1):450-465. doi: 10.1109/JIOT.2017.2750180
[2]	LIU C H, TSAI H C. On the Limits of Coexisting Coverage and Capacity in Multi-RAT Heterogeneous Networks[J]. IEEE Transactions on Wireless Communications, 2017,16(5):3086-3101. doi: 10.1109/TWC.2017.2675399
[3]	ALTHUMALI H, OTHMAN M. A Survey of Random Access Control Techniques for Machine-to-Machine Communications in LTE/LTE-A Networks[J]. IEEE Access, 2018,6:74961-74983. doi: 10.1109/Access.6287639
[4]	TRAJANO A F R, De SOUSA A A M, RODRIGUES E B, et al. Leveraging Mobile Edge Computing on Smart Grids Using LTE Cellular Networks[C]// Proceedings of the 2019 International Symposium on Computers and Communications. Piscataway: IEEE, 2019: 8969784.
[5]	YU S, CHEN X, YANG L, et al. Intelligent Edge: Leveraging Deep Imitation Learning for Mobile Edge Computation Offloading[J]. IEEE Wireless Communications, 2020,27(1):92-99. doi: 10.1109/MWC.7742
[6]	ALTINEL D, KURT G K. Modeling of Hybrid Energy Harvesting Communication Systems[J]. IEEE Transactions on Green Communications and Networking, 2019,3(2):523-534. doi: 10.1109/TGCN
[7]	WU Y, QIAN L P, NI K, et al. Delay-Minimization Nonorthogonal Multiple Access Enabled Multi-user Mobile Edge Computation Offlfloading[J]. IEEE Journal of Selected Topics in Signal Processing, 2019,13(3):392-407. doi: 10.1109/JSTSP.4200690
[8]	WANG C, YU F R, LIANG C, et al. Joint Computation Offloading and Interference Management in Wireless Cellular Networks with Mobile Edge Computing[J]. IEEE Transactions on Vehicular Technology, 2017,66(8):7342-7445.
[9]	ZHANG K, MAO Y, LENG S, et al. Energy Efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks[J]. IEEE Access, 2016,4:5896-5907. doi: 10.1109/ACCESS.2016.2597169
[10]	TANZIL S M S, GHAREHSHIRAN O N, KRISHNAMURTHY V. Femto-cloud Formation: A Coalitional Game-theoretic Approach[C]// Proceedings of the 2015 IEEE Global Communications Conference. Piscataway: IEEE, 2015: 7417264.
[11]	OUEIS J, STRINATI E C, BARBAROSSA S. Small Cell Clustering for Efficient Distributed Cloud Computing[C]// Proceedings of the 2014 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. Piscataway: IEEE, 2014: 1474-1479.
[12]	OUEIS J, STRINATI E C, SARDELLITTI S, et al. Small Cell Clustering for Efficient Distributed Fog Computing: A Multi-user Case[C]// Proceedings of the 2015 IEEE Vehicular Technology Conference. Piscataway: IEEE, 2015: 7391144.
[13]	XIAO Y, KRUNZ M. QoE and Power Efficiency Trade off for Fog Computing Networks with Fog Node Cooperation[C]// Proceedings of the 2017 IEEE Conference on Computer Communications. Piscataway: IEEE, 2017: 1-9.
[14]	WANG S, ZAFER M, LEUNG K K. Online Placement of Multi-Component Applications in Edge Computing Environments[J]. IEEE Access, 2017,5:2514-2533. doi: 10.1109/ACCESS.2017.2665971
[15]	MACH P, BECVAR Z. Mobile Edge Computing: A Survey on Architecture and Computation Offloading[J]. IEEE Communications Surveys and Tutorials, 2017,19(3):1628-1656. doi: 10.1109/COMST.2017.2682318
[16]	KHAN A U R, OTHMAN M, MADANI S A, et al. A Survey of Mobile Cloud Computing Application Models[J]. IEEE Communications Surveys and Tutorials, 2014,16(1):393-413. doi: 10.1109/SURV.2013.062613.00160
[17]	SCUTARI G, FACCHINEI F, LAMPARIELLO L, et al. Parallel and Distributed Methods for Constrained Non-convex Optimization Part I: Theory[J]. IEEE Transactions on Signal Processing, 2017,65(8):1929-1944. doi: 10.1109/TSP.78
[18]	CHEN X, JIAO L, LI W, et al. Efficient Multi-user Computation Offloading for Mobile-edge Cloud Computing[J]. IEEE/ACM Transactions on Networking, 2016,24(5):2795-2808. doi: 10.1109/TNET.2015.2487344
[19]	XIAO Y, KRUNZ M. Distributed Optimization for Energy-efficient Fog Computing in the Tactile Internet[J]. IEEE Journal on Selected Areas in Communications, 2018,36(11):2390-2400. doi: 10.1109/JSAC.2018.2872287
[20]	MAO Y, YOU C, ZHANG J, et al. A Survey on Mobile Edge Computing: The Communication Perspective[J]. IEEE Communications Surveys and Tutorials, 2017,19(4):2322-2358. doi: 10.1109/COMST.2017.2745201
[21]	SCUTARI G, FACCHINEI F, LAMPARIELLO L, et al. Parallel and Distributed Methods for Constrained Non-convex Optimization Part II: Applications in Communications and Machine Learning[J]. IEEE Transactions on Signal Processing, 2017,65(8):1945-1960. doi: 10.1109/TSP.78
[22]	3GPP. Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access(E-UTRA); Further Advancements for E-UTRA Physical Layer Aspects (Release 9): 3GPP TR 36.814 v.9.0.0. [R]. Valbonne: 3GPP, 2010.

参数	数值	参数	数值
K	[0;20]	p^max/dBm	23
N	1, 3, 6	p^DL/dBm	20, 30, 46
θ	0.2	W_k/Mbit	[0; 50]
τ_k/s	10	f_n/GHz	[50; 200]
δ²/dBm	-125	X_k/(周期·比特^-1)	2300

Research and experiment on multi-user computational offloading based on mobile edge computing

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