|Table of Contents|

A Service Offloading Method for Resource Utilization and PrivacyPreservation Trade-offs in 5G-Enabled Edge Computing(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2021年04期
Page:
102-110
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
A Service Offloading Method for Resource Utilization and PrivacyPreservation Trade-offs in 5G-Enabled Edge Computing
Author(s):
Li Bangyuan1Zhang Chunhui1Chang Rong1Chen Jun1Xu Xiaolong2
(1.Yunnan Power Grid Corporation,Yuxi Power Supply Bureau,Yuxi 653100,China)(2.School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China)
Keywords:
5Gservice offloadingedge computingload balanceprivacy preservation
PACS:
G301
DOI:
10.3969/j.issn.1001-4616.2021.04.013
Abstract:
In view of the problems of hardware resource waste and service privacy leakage in the current 5G environment,a service offloading method(SOM)for resource utilization and privacy preservation trade-offs has been designed to achieve efficient utilization of edge nodes and protection of user privacy. Specifically,resource utilization and privacy protection requirements of edge nodes in service offloading scenario have been firstly analyzed and modeled,and then defined as a multi-objective optimization problem. Afterward,the feasible solutions of the problem are generated by utilizing the improving the strength pareto evolutionary algorithm(SPEA2). Furthermore,simple additive weighting(SAW)and multi-criteria decision-making method(MCDM)are deployed to select the global optimal solution from multiple feasible solutions. Finally,the experimental results show the effectiveness of the proposed SOM.

References:

[1] 赵梓铭,刘芳,蔡志平,等. 边缘计算:平台、应用与挑战[J]. 计算机研究与发展,2018,55(2):327-337.
[2]IBRAHIM H,IBRA Y,BADRUL A,et al. The rise of “big data”on cloud computing:review and open research issues[J]. Information systems,2015,47:98-115.
[3]MARIA P R,MISCHA D,ALFREDO G,et al. Internet of things in the 5g era:enablers,architecture,and business models[J]. IEEE journal on selected areas in communications,2016,34(3):510-527.
[4]Akhil G,Rakesh J K. A survey of 5G network:architecture and emerging technologies[J]. IEEE Access,2015,3:1206-1232.
[5]HE Q,CUI G M,ZHANG X Y,et al. A game-theoretical approach for user allocation in edge computing environment[J]. IEEE transactions on parallel and distributed systems(TPDS),2019,31(3):515-529.
[6]GAO H H,MIAO H K,LIU L,et al. Automated quantitative verification for service-based system design:a visualization transform tool perspective[J]. International journal of software engineering and knowledge engineering,2018,28(10):1369-1397.
[7]WANG Y C,HE Q,YE D Y,et al. Formulating criticality-based cost-effective fault tolerance strategies for multi-tenant service-based systems[J]. IEEE transactions on software engineering,2017,44(3):291-307.
[8]林俊宇,王慧强,马春光,等. 一种基于DAG动态重构的认知网络服务迁移方法[J]. 软件学报,2014,25(10):2373-2384.
[9]彭长根,丁红发,朱义杰,等. 隐私保护的信息熵模型及其度量方法[J]软件学报,2016,27(8):1891-1903.
[10]朱永红,丁恩杰. 负载均衡的异构WMSN节点布局方法[J]. 通信学报,2015,36(10):157-164.
[11]QIAN Y F,HU L,CHEN J,et al. Privacy-aware service placement for mobile edge computing via federated learning[J]. Information sciences,2019,505:562-570.
[12]LU R X,KEVIN H,ARASH L H,et al. A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced iot[J]. IEEE access,2017,5:3302-3312.
[13]DU M,WANG K,XIA Z Q,et al. Differential privacy preserving of training model in wireless big data with edge computing[J]. IEEE transactions on big data,2018,6(2):283-295.
[14]DING L,MALEK S B. A novel architecture for automatic classification for effective security in edge computing environments[C]//2018 IEEE/ACM Symposium on Edge Computing(SEC),Singapore:IEEE,2018:416-420. doi:10.1109/SEC.2018.00056.
[15]HE X F,LIU J,JIN R C,et al. Privacy-aware offloading in mobile-edge computing[C]//GLOBECOM 2017-2017 IEEE Global Communications Conference,Singapore:IEEE,2017:1-6.
[16]MAO Y Y,ZHANG J,KHALED L B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE journal on selected areas in communications,2016,34(12):3590-3605.
[17]CHEN X,JIAO L,LI W Z,et al. Efficient multi-user computation offloading for mobile-edge cloud computing[J]. IEEE/ACM transactions on networking,2015,24(5):2795-2808.
[18]TAO X Y,KAORU O,DONG M X,et al. Performance guaranteed computation offloading for mobile-edge cloud computing[J]. IEEE wireless communications letters,2017,6(6):774-777.
[19]TIAN H,JIANG L,ALEXEY V,et al. Selective offloading in mobile edge computing for the green internet of things[J]. IEEE network,2018,32(1):54-60.
[20]ZHANG K,MAO Y M,LENG S P,et al. Optimal delay constrained offloading for vehicular edge computing networks[C]//2017 IEEE International Conference on Communications(ICC),Paris:IEEE,2017:1-6.
[21]WANG F,XU J,WANG X,et al. Joint offloading and computing optimization in wireless powered mobile-edge computing systems[J]. IEEE transactions on wireless communications,2017,17(3):1784-1797.
[22]WANG Y T,SHENG M,WANG X J,et al. Mobile-edge computing:partial computation offloading using dynamic voltage scaling[J]. IEEE transactions on communications,2016,64(10):4268-4282.
[23]CHEN M,HAO Y X. Task offloading for mobile edge computing in software defined ultra-dense network[J]. IEEE journal on selected areas in communications,2018,36(3):587-597.
[24]YU S,LANGAR R,FU X M,et al. Computation offloading with data caching enhancement for mobile edge computing[J]. IEEE transactions on vehicular technology,2018,67(11):11098-11112.
[25]YUAN D,YANG R,LIU R,et al. A data placement strategy in scientific cloud workflows[J]. Future generation computer systems,2010,26(8):1200-1214.

Memo

Memo:
-
Last Update: 2021-12-15