东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2013, Vol. 34 ›› Issue (4): 482-485.DOI: -

• 信息与控制 • 上一篇    下一篇

基于同时向上叫价拍卖的云资源分配方法与竞价策略

王学毅1,2，王兴伟2，黄敏2   

  1. (1.东北大学软件学院，辽宁沈阳110819；2.东北大学信息科学与工程学院，辽宁沈阳110819)
  • 收稿日期:2012-10-26 修回日期:2012-10-26 出版日期:2013-04-15 发布日期:2013-06-19
  • 通讯作者: 王学毅
  • 作者简介:王学毅(1979-)，男，辽宁辽阳人，东北大学讲师，博士研究生；王兴伟(1968-)，男，辽宁盖州人，东北大学教授，博士生导师；黄敏(1968-)，女，福建长乐人，东北大学教授，博士生导师.
  • 基金资助:
    国家杰出青年科学基金资助项目(61225012)；国家自然科学基金资助项目(61070162，71071028，70931001)；高等学校博士学科点专项科研基金资助项目(20100042110025，20110042110024)；工信部物联网发展专项资金资助项目；中央高校基本科研业务费专项资金资助项目(N110204003).

Cloud Resource Allocation Method and Bidding Strategy Based on Simultaneous Upward Bidding Auction

WANG Xueyi1,2， WANG Xingwei2， HUANG Min2   

  1. 1. School of Software， Northeastern University， Shenyang 110819， China; 2. School of Information Science ＆ Engineering， Northeastern University， Shenyang 110819， China.
  • Received:2012-10-26 Revised:2012-10-26 Online:2013-04-15 Published:2013-06-19
  • Contact: WANG Xueyi
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摘要： 针对云资源的特点，基于微观经济学方法和支持向量回归机算法，提出了一种云资源分配方法与竞价策略.首先，采用块状能力来描述买方需求、卖方资源.然后，建立云资源系统框架，根据各个买方资源交易历史记录基于支持向量回归机算法来预测各个买方的竞价信息.在此基础上，利用非完全信息纳什均衡理论，设计了同时向上叫价拍卖和用户竞价策略，确定了最终成交价格，并根据成交价格来分配云资源.仿真结果表明，所提出的云资源分配方法和策略是可行和有效的.

关键词: 同时向上叫价拍卖, 价格预测, 支持向量回归, 云计算, 纳什均衡

Abstract: On account of the characteristics of cloud resources， a cloud resource allocation method and bidding strategy were proposed on the basis of the microeconomic method and support vector regression method. Firstly， the block ability was used to describe buyer demand and seller resources. Secondly， the system architecture of cloud resource was constructed， and according to the resource transaction history of each buyer， their bidding price was predicted by using support vector regression method. Based on the above， according to Nash equilibrium theory of incomplete information， simultaneous upward bidding auction was designed， and the final transaction price was formed. What’s more， cloud resources were allocated by the final transaction price. Simulation results showed that the proposed method and strategy were feasible and effective.

Key words: simultaneous upward bidding auction, price prediction, support vector regression, cloud computing, Nash equilibrium

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