Nash Equilibrium and Decentralized Pricing for QoS Aware Service Composition in Cloud Computing Environments

被引:8
作者
Pan, Li [1 ]
An, Bo [2 ]
Liu, Shijun [1 ]
Cui, Lizhen [1 ]
机构
[1] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Shandong, Peoples R China
[2] Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore
来源
2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017) | 2017年
基金
中国国家自然科学基金;
关键词
cloud; service composition; non-cooperative game; Nash equilibrium; pricing; bidding;
D O I
10.1109/ICWS.2017.28
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
QoS aware service composition necessitates an effective pricing mechanism in regulating service providers in public cloud computing environments. However, due to the fact that service providers are usually autonomous, strategic and self-motivated, it is far from trivial to deal with the pricing issues between them. In this paper we formulate a non-cooperative service pricing game to understand the performance of a QoS aware service composition model, for which multiple providers strategically bid how to provide and price their elementary services and establish the Nash equilibrium as the final service composition scheme. We also develop a proportional revenue division rule to incentivize elementary service providers to contribute in improving the QoS of the final composite service delivered to end users. Concerning privacy conservation, we develop a decentralized and recursive bidding algorithm, allowing service providers to reach an equilibrium without disclosing their private information. Through theoretical analysis, we show that a Nash equilibrium exists in a QoS aware service composition game. Through extensive simulations, we show that the proposed recursive bidding process can converge quickly to a Nash equilibrium service composition scheme, and its efficiency is generally high.
引用
收藏
页码:154 / 163
页数:10
相关论文
共 27 条
[1]  
An B., 2010, Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: Volume 1 - Volume 1, AAMAS '10, V1, P981
[2]  
[Anonymous], 2011, P INT C HIGH PERF CO
[3]   EPISTEMIC CONDITIONS FOR NASH EQUILIBRIUM [J].
AUMANN, R ;
BRANDENBURGER, A .
ECONOMETRICA, 1995, 63 (05) :1161-1180
[4]  
AuYoung A., 2006, Proceedings of the 15th IEEE International Symposium on High Performance Distributed Computing (IEEE Cat. No.06TH8878), P119
[5]  
Bonnans JF, 2006, NUMERICAL OPTIMIZATI, V2nd, DOI 10.1007/978-3-662-05078-1
[6]   RenderVerse: Hybrid Render Farm for Cluster and Cloud Environments [J].
Cho, KyungWoon ;
Seo, JinWoo ;
Kang, Joon ;
Lee, JinUng ;
Kim, SangJin ;
Park, JinWoo ;
Song, JiYoung ;
Kim, JungWhan ;
Kwon, DaeSuk .
2014 7TH CONFERENCE ON CONTROL AND AUTOMATION (CA), 2014, :6-11
[7]   SPEEDUP VERSUS EFFICIENCY IN PARALLEL SYSTEMS [J].
EAGER, DL ;
ZAHORJAN, J ;
LAZOWSKA, ED .
IEEE TRANSACTIONS ON COMPUTERS, 1989, 38 (03) :408-423
[8]   Semantics-based dynamic service composition [J].
Fujii, K ;
Suda, T .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2005, 23 (12) :2361-2372
[9]   Trust-oriented QoS-aware composite service selection based on genetic algorithms [J].
Gao, Hao ;
Yan, Jun ;
Mu, Yi .
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2014, 26 (02) :500-515
[10]  
Haifeng Li, 2011, Proceedings of the 2011 IEEE International Conference on Web Services (ICWS 2011), P444, DOI 10.1109/ICWS.2011.45