Full Text:  <2937>

Summary:  <1168>

CLC number: TP311.5

On-line Access: 2021-02-01

Received: 2019-12-23

Revision Accepted: 2020-06-30

Crosschecked: 2020-12-11

Cited: 0

Clicked: 3810

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yao Xia

https://orcid.org/0000-0001-5551-2570

Zhiqiu Huang

https://orcid.org/0000-0001-6843-1892

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering 

Accepted manuscript available online (unedited version)


A strategy-proof auction mechanism for service composition based on user preferences


Author(s):  Yao Xia, Zhiqiu Huang

Affiliation(s):  College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Corresponding email(s):  xiayao@nuaa.edu.cn, zqhuang@nuaa.edu.cn

Key Words:  Combinatorial reverse auction, Service composition, User preference, Strategy-proof, Dynamic pricing


Share this article to: More <<< Previous Paper|Next Paper >>>

Yao Xia, Zhiqiu Huang. A strategy-proof auction mechanism for service composition based on user preferences[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.1900726

@article{title="A strategy-proof auction mechanism for service composition based on user preferences",
author="Yao Xia, Zhiqiu Huang",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.1900726"
}

%0 Journal Article
%T A strategy-proof auction mechanism for service composition based on user preferences
%A Yao Xia
%A Zhiqiu Huang
%J Frontiers of Information Technology & Electronic Engineering
%P 185-201
%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.1900726"

TY - JOUR
T1 - A strategy-proof auction mechanism for service composition based on user preferences
A1 - Yao Xia
A1 - Zhiqiu Huang
J0 - Frontiers of Information Technology & Electronic Engineering
SP - 185
EP - 201
%@ 2095-9184
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/FITEE.1900726"


Abstract: 
service composition is an effective method of combining existing atomic services into a value-added service based on cost and quality of service (QoS). To meet the diverse needs of users and to offer pricing services based on QoS, we propose a service composition auction mechanism based on user preferences, which is strategy-proof and can be beneficial in selecting services based on user preferences and dynamically determining the price of services. We have proven that the proposed auction mechanism achieves desirable properties including truthfulness and individual rationality. Furthermore, we propose an auction algorithm to implement the auction mechanism, and carry out extensive experiments based on real data. The results verify that the proposed auction mechanism not only achieves desirable properties, but also helps users find a satisfactory service composition scheme.

考虑用户偏好的服务组合防策略拍卖机制


夏瑶,黄志球
南京航空航天大学计算机科学与技术学院,中国南京市,210016

摘要:服务组合是一种基于服务成本和服务质量(QoS)将现有原子服务组合为增值服务的有效方法。为满足用户的多样化需求,提供基于QoS的定价服务,提出一种基于用户偏好的服务组合拍卖机制,该机制具有防策略性,有利于根据用户偏好选择服务,动态确定服务价格。本文证明,所提出的拍卖机制达到了期望的性质,包括真实性和个体合理性。此外,提出一种拍卖算法来实现拍卖机制,并在真实数据基础上进行大量实验。结果表明,所提出的拍卖机制不仅达到预期效果,而且帮助用户找到满意的服务组合方案。

关键词组:组合逆向拍卖;服务组合;用户偏好;防策略性;动态定价

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Al-Masri E, Mahmoud QH, 2007. Discovering the best web service. Proc 16th Int Conf on World Wide Web, p.1257-1258.

[2]Borjigin W, Ota K, Dong MX, 2018. In broker we trust: a double-auction approach for resource allocation in NFV markets. IEEE Trans Netw Serv Manag, 15(4):1322-1333.

[3]Deng SG, Huang LT, Taheri J, et al., 2016. Mobility-aware service composition in mobile communities. IEEE Trans Syst Man Cybern Syst, 47(3):555-568.

[4]Dimitriou T, Krontiris I, 2017. Privacy-respecting auctions and rewarding mechanisms in mobile crowd-sensing applications. J Netw Comput Appl, 100:24-34.

[5]Dong WY, Zhou MC, 2017. A supervised learning and control method to improve particle swarm optimization algorithms. IEEE Trans Syst Man Cybern Syst, 47(7):1135-1148.

[6]Ghahramani MH, Zhou MC, Hon CT, 2017. Toward cloud computing QoS architecture: analysis of cloud systems and cloud services. IEEE/CAA J Autom Sin, 4(1):6-18.

[7]He Q, Yan J, Jin H, et al., 2014. Quality-aware service selection for service-based systems based on iterative multi-attribute combinatorial auction. IEEE Trans Softw Eng, 40(2):192-215.

[8]Jiang CX, Chen Y, Wang Q, et al., 2018. Data-driven auction mechanism design in IaaS cloud computing. IEEE Trans Serv Comput, 11(5):743-756.

[9]Karakaya G, Köksalan M, 2011. An interactive approach for multi-attribute auctions. Dec Support Syst, 51(2):299-306.

[10]Kennedy J, Eberhart R, 1995. Particle swarm optimization. Proc IEEE Int Conf on Neural Networks, p.1942-1948.

[11]Li J, Zhang JQ, Jiang CJ, et al., 2015. Composite particle swarm optimizer with historical memory for function optimization. IEEE Trans Cybern, 45(10):2350-2363.

[12]Moghaddam M, 2012. An auction-based approach for composite web service selection. Int Workshops on Service Oriented Computing, p.400-405.

[13]Moghaddam M, Davis JG, 2017. Auction-based models for composite service selection: a design framework. Proc 5th and 6th Australian Symp on Service Research and Innovation, p.101-115.

[14]Moghaddam M, Davis JG, Viglas T, 2013. A combinatorial auction model for composite service selection based on preferences and constraints. IEEE Int Conf on Services Computing, p.81-88.

[15]Mu’Alem A, Nisan N, 2008. Truthful approximation mechanisms for restricted combinatorial auctions. Game Econ Behav, 64(2):612-631.

[16]Nisan N, Roughgarden T, Tardos E, et al., 2007. Algorithmic Game Theory. Cambridge University Press, Cambridge, USA.

[17]Prasad AS, Rao S, 2014. A mechanism design approach to resource procurement in cloud computing. IEEE Trans Comput, 63(1):17-30.

[18]Sha J, Du YY, Qi L, 2019. A user requirement oriented web service discovery approach based on logic and threshold Petri net. IEEE/CAA J Autom Sin, 6(6):1528-1542.

[19]Shi B, Wang JW, Wang ZW, et al., 2017. Trading web services in a double auction-based cloud platform: a game theoretic analysis. Proc IEEE 14th Int Conf on Services Computing, p.76-83.

[20]Singer Y, 2010. Budget feasible mechanisms. Proc 51st Annual Symp on Foundations of Computer Science, p.765-774.

[21]Tanaka M, Murakami Y, 2016. Strategy-proof pricing for cloud service composition. IEEE Trans Cloud Comput, 4(3):363-375.

[22]Wang PW, Du XY, 2019. QoS-aware service selection using an incentive mechanism. IEEE Trans Serv Comput, 12(2):262-275.

[23]Wang PW, Ding ZJ, Jiang CJ, et al., 2014. Constraint-aware approach to web service composition. IEEE Trans Syst Man Cybern Syst, 44(6):770-784.

[24]Wang PW, Ding ZJ, Jiang CJ, et al., 2016. Automatic web service composition based on uncertainty execution effects. IEEE Trans Serv Comput, 9(4):551-565.

[25]Wang PW, Liu T, Zhan Y, et al., 2017a. A Bayesian Nash equilibrium of QoS-aware web service composition. IEEE Int Conf on Web Services, p.676-683.

[26]Wang PW, Zhan Y, Liu T, et al., 2017b. QoS-aware service composition for service-based systems using multi-round vickery auction. IEEE Int Conf on Systems, Man, and Cybernetics, p.2891-2896.

[27]Watanabe A, Ishikawa F, Fukazawa Y, et al., 2012. Web service selection algorithm using Vickrey auction. Proc IEEE 19th Int Conf on Web Services, p.336-342.

[28]Wei Y, Pan L, Yuan D, et al., 2016. A distributed game-theoretic approach for IaaS service trading in an auction-based cloud market. IEEE TrustCom/BigDataSE/ISPA, p.1543-1550.

[29]Wen YT, Shi JY, Zhang Q, et al., 2015. Quality-driven auction-based incentive mechanism for mobile crowd sensing. IEEE Trans Veh Technol, 64(9):4203-4214.

[30]Wu QW, Zhou MC, Zhu QS, et al., 2018. VCG auction-based dynamic pricing for multigranularity service composition. IEEE Trans Autom Sci Eng, 15(2):796-805.

[31]Wu QW, Zhou MC, Zhu QS, et al., 2020. MOELS: multiobjective evolutionary list scheduling for cloud workflows. IEEE Trans Autom Sci Eng, 17(1):166-176.

[32]Wu Y, Yan CG, Ding ZJ, et al., 2016. A multilevel index model to expedite web service discovery and composition in large-scale service repositories. IEEE Trans Serv Comput, 9(3):330-342.

[33]Xu J, Xiang JX, Yang DJ, 2015. Incentive mechanisms for time window dependent tasks in mobile crowdsensing. IEEE Trans Wirel Commun, 14(11):6353-6364.

[34]Zhang Y, Zhou P, Cui GM, 2019. Multi-model based PSO method for burden distribution matrix optimization with expected burden distribution output behaviors. IEEE/CAA J Autom Sin, 6(6):1506-1512.

[35]Zheng ZZ, Gui Y, Wu F, et al., 2015. STAR: strategy-proof double auctions for multi-cloud, multi-tenant bandwidth reservation. IEEE Trans Comput, 64(7):2071-2083.

[36]Zheng ZZ, Wu F, Gao XF, et al., 2017. A budget feasible incentive mechanism for weighted coverage maximization in mobile crowdsensing. IEEE Trans Mob Comput, 16(9):2392-2407.

[37]Zhou BW, Srirama SN, Buyya R, 2019. An auction-based incentive mechanism for heterogeneous mobile clouds. J Syst Softw, 152:151-164.

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE