|Table of Contents|

Service Selection Algorithm Based on Latency-Aware in a Multiple Edge Server Cooperation Environment(PDF)

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

Issue:
2022年02期
Page:
126-135
Research Field:
·计算机科学与技术·
Publishing date:

Info

Title:
Service Selection Algorithm Based on Latency-Aware in a Multiple Edge Server Cooperation Environment
Author(s):
Xie Na1Tan Wenan12Sun Yong3Zhao Lu1Huang Li14
(1.School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)(2.School of Computer and Information,Shanghai Polytechnic University,Shanghai 201209,China)(3.School of Geographical Sciences,Nanjing N
Keywords:
mobile edge computingheuristic algorithmlatency-awareservice selection
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2022.02.016
Abstract:
Mobile edge computing can provide low latency services for users.However,as the requirements of users become increasingly complex and diverse,it is difficult for a single edge server to meet their needs. Therefore,the service selection problem in a multiple edge server cooperation environment has become a hot issue in the field of service computing. In this paper,the problem is modeled as a constrained optimization problem,and then a heuristic service selection algorithm named the LLMES algorithm is proposed. The algorithm selects the neighbor nodes of the current local server as candidate servers according to the Dijkstra algorithm in the edge server network,chooses an edge server that provides the most effective services with the least latency for users as the current optimal server based on the greedy selection strategy of low latency and multiple effective services. Thus,a group of cooperative edge servers is selected to provide services for users,that is,a group of services that meet the requirement of users is selected. Finally,experimental results show that the performance of the LLMES algorithm proposed in this paper outperforms significantly three representative approaches.

References:

[1] ZANELLA A,BUI N,CASTELLANI A,et al. Internet of Things for smart cities[J]. IEEE internet of things journal,2014,1(1):22-32.
[2]DEBAUCHE O,MAHMOUDI S,MAHMOUDI S A,et al. Edge computing and artificial intelligence for real-time poultry monitoring[J]. Procedia computer,2020,175(1):534-541.
[3]崔勇,宋健,缪葱葱,等. 移动云计算研究进展与趋势[J]. 计算机学报,2017,40(2):273-295.
[4]DENG S G,HUANG L T,HU D N,et al. Mobility-enabled service selection for composite services[J]. IEEE transactions on services computing,2016,9(3):394-407.
[5]YU H Y,LIU C Y,REN Y L,et al. Service node selection optimization for mobile crowd sensing in a road network environment[J]. Vehicular communications,2020,22(4):100203.1-100203.14.
[6]DENG S G,WU H Y,TAN W,et al. Mobile service selection for composition:an energy consumption perspective[J]. IEEE transactions on automation science and engineering,2017,14(3):1478-1490.
[7]TONG E D,CHEN L,LI H Z. Energy-aware service selection and adaptation in wireless sensor networks with QoS guarantee[J]. IEEE transactions on services computing,2020,13(5):829-842.
[8]ZHANG W W,WEN Y G. Energy-efficient task execution for application as a general topology in mobile cloud computing[J]. IEEE transactions on cloud computing,2015,6(3):708-719.
[9]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.
[10]HU Y C,PATEL M,SABELLA D,et al. Mobile edge computing—a key technology towards 5G[J]. ETSI white paper,2015,11(11):1-16.
[11]MAO Y Y,YOU C S,ZHANG J,et al. A survey on mobile edge computing:the communication perspective[J]. IEEE communications surveys & tutorials,2017,19(4):2322-2358.
[12]乐光学,戴亚盛,杨晓慧,等. 边缘计算可信协同服务策略建模[J]. 计算机研究与发展,2020,57(5):1080-1102.
[13]WU H Y,DENG S G,LI W,et al. Service selection for composition in mobile edge computing systems[C]//2018 IEEE international conference on Web services,San Francisco,CA,USA,2018:355-358.
[14]刘伟,黄宇成,杜薇,等. 移动边缘计算中资源受限的串行任务卸载策略[J]. 软件学报,2020,31(6):309-328.
[15]XIE N,TAN W A,ZHENG X R,et al. An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing[J]. Journal of industrial information integration,2021,23(2):1-12.
[16]GUO H Z,LIU J J. Collaborative computation offloading for multi-access edge computing over fiber-wireless networks[J]. IEEE transactions on vehicular technology,2018,67(5):4514-4526.
[17]CHEN L X,ZHOU S,XU J. Computation peer offloading for energy-constrained mobile edge computing in small-cell networks[J]. IEEE/ACM transactions on networking,2018,26(4):1619-1632.
[18]DENG S G,XIANG Z,TAHERI J,et al. Optimal application deployment in resource constrained distributed edges[J]. IEEE transactions on mobile computing,2020,20(5):1907-1923.
[19]GUI G M,HE Q,CHEN F F,et al. Trading off between multi-tenancy and interference:a service user allocation game[J/OL]. IEEE transactions on services computing,2020. Available:10.1109/TSC.2020.3028760.
[20]DENG S G,XIANG Z Z,ZHAO P,et al. Dynamical resource allocation in edge for trustable IoT systems:a reinforcement learning method[J]. IEEE transactions on industrial informatics,2020,16(9):6103-6113.
[21]XIA X Y,CHEN F F,HE Q,et al. Cost-effective app data distribution in edge computing[J]. IEEE transactions on parallel and distributed systems,2021,32(1):31-44.
[22]PASTERIS S,WANG S Q,HERBSTER M,et al. Service placement with provable guarantees in heterogeneous edge computing systems[C]//IEEE conference on computer communications. Chengdu,China,2019:514-522.

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