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
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", %0 Journal Article TY - JOUR
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: 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. 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 |
Open peer comments: Debate/Discuss/Question/Opinion
<1>