离群弹性Web服务QoS预测
Outlier-Resilient Web Service QoS Prediction
摘要：
Web服务的激增使得用户很难在众多功能相同或相似的服务候选者中选择最合适的一个。服务质量（QoS）描述了Web服务的非功能特性，它已成为服务选择的关键区别。然而，由于时间开销和资源开销巨大，用户无法调用所有的Web服务来获得相应的QoS值。因此，预测未知的QoS值是至关重要的。虽然已经提出了各种QoS预测方法，其中很少有考虑离群值，这可能会显着降低预测性能。为了克服这一局限性，本文提出了一种离群弹性QoS预测方法。我们的方法利用柯西损失来衡量所观察到的QoS值和预测值之间的差异。由于柯西损失的鲁棒性，我们的方法对离群值有弹性。我们进一步扩展我们的方法，以提供时间感知的QoS预测结果，考虑到时间信息。最后，我们在静态和动态数据集上进行了大量的实验。结果表明，我们的方法是能够实现更好的性能比国家的最先进的基线方法。
英文摘要：
The proliferation of Web services makes it difficult for users to select the most appropriate one among numerous functionally identical or similar service candidates. Quality-of-Service (QoS) describes the non-functional characteristics of Web services, and it has become the key differentiator for service selection. However, users cannot invoke all Web services to obtain the corresponding QoS values due to high time cost and huge resource overhead. Thus, it is essential to predict unknown QoS values. Although various QoS prediction methods have been proposed, few of them have taken outliers into consideration, which may dramatically degrade the prediction performance. To overcome this limitation, we propose an outlier-resilient QoS prediction method in this paper. Our method utilizes Cauchy loss to measure the discrepancy between the observed QoS values and the predicted ones. Owing to the robustness of Cauchy loss, our method is resilient to outliers. We further extend our method to provide time-aware QoS prediction results by taking the temporal information into consideration. Finally, we conduct extensive experiments on both static and dynamic datasets. The results demonstrate that our method is able to achieve better performance than state-of-the-art baseline methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
