A Learning Approach to QoS Prediction via Multi-Dimensional Context
A Learning Approach to QoS Prediction via Multi-Dimensional Context
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DOI：
10.1109/icws.2017.29
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Wei Xiong;Zhao Wu;Bing Li;Qiong Gu
中科院分区：
文献类型：
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作者：
Wei Xiong;Zhao Wu;Bing Li;Qiong Gu
移动的Internet技术的发展使得Web服务的客户端能够适应上下文的变化，这就需要对Web服务的QoS进行监控。大多数现代QoS预测方法利用一个特定维度的QoS特性，例如，时间或地点，没有利用上下文多维之间的复杂关系。本文提出了一种学习方法，通过多维上下文从过去的调用历史中获得的Web服务的服务质量（QoS）预测。为了验证我们的方法，大规模的实验进行了基于真实世界的Web服务数据集，WSDESTOM。结果表明，我们提出的方法实现了更高的预测精度比其他方法。
Advances in mobile Internet technology have enabled the clients of Web services to be able to adjust to context changes, which need to rely on monitoring to QoS of Web services. Most contemporary QoS prediction methods exploit the QoS characteristics for one specific dimension, e.g., time or location, and do not exploit the complicated relations among multi-dimension of context. This paper proposes a learning approach to quality-of-service (QoS) prediction of web services via multi-dimensional context derived from the past invocation history. To validate our approach, large-scale experiments are conducted based on a real-world Web service dataset, WSDream. The results show that our proposed approach achieves higher prediction accuracy than other approaches.