期刊
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
卷 24, 期 6, 页码 1213-1222出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2012.285
关键词
Quality-of-service; cloud service; ranking prediction; personalization
资金
- National Basic Research Program of China (973 Project) [2011CB302603]
- National Natural Science Foundation of China [61100078, 61073132]
- Research Grants Council of the Hong Kong Special Administrative Region, China [CUHK 415311]
- Shenzhen Basic Research Program [JC201104220300A]
Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据