FES：一个快速有效的可扩展QoS预测框架
FES: A Fast Efficient Scalable QoS Prediction Framework
摘要：
Web服务的服务质量预测在服务生存周期的各个方面，如服务组合、服务选择、服务推荐等都有着广泛的应用，是服务计算的重要组成部分。设计QoS预测算法的主要目标之一是达到令人满意的预测精度。然而，准确性不是开发QoS预测算法时要满足的唯一标准。该算法在预测时间方面必须更快，以便它可以集成到实时推荐或合成系统中。设计预测算法时要考虑的另一个重要因素是可扩展性，以确保预测算法可以处理大规模数据集。现有的QoS预测算法往往在保证其他目标的同时，对一个目标进行妥协。在本文中，我们提出了一个半离线的QoS预测模型，同时实现三个重要目标：更高的准确性，更快的预测时间，可扩展性。在这里，我们的目标是预测不同的用户服务的QoS值。我们的框架由多阶段预测算法组成：预处理阶段预测，在线预测和使用预训练模型的预测。在预处理阶段，我们首先对数据集进行多级聚类，以获得相关的用户和服务。然后，我们预处理集群使用协同过滤，以消除给定的QoS调用日志矩阵的稀疏性。最后，我们建立了一个两阶段，半离线回归模型，使用神经网络预测的QoS值的服务，由用户在实时调用。我们在四个公开的WS-DREAM数据集上的实验结果表明，与最先进的方法相比，我们的框架在准确性，可扩展性，快速响应性方面具有效率。
英文摘要：
Quality-of-Service prediction of web service is an integral part of services computing due to its diverse applications in the various facets of a service life cycle, such as service composition, service selection, service recommendation. One of the primary objectives of designing a QoS prediction algorithm is to achieve satisfactory prediction accuracy. However, accuracy is not the only criteria to meet while developing a QoS prediction algorithm. The algorithm has to be faster in terms of prediction time so that it can be integrated into a real-time recommendation or composition system. The other important factor to consider while designing the prediction algorithm is scalability to ensure that the prediction algorithm can tackle large-scale datasets. The existing algorithms on QoS prediction often compromise on one goal while ensuring the others. In this paper, we propose a semi-offline QoS prediction model to achieve three important goals simultaneously: higher accuracy, faster prediction time, scalability. Here, we aim to predict the QoS value of service that varies across users. Our framework consists of multi-phase prediction algorithms: preprocessing-phase prediction, online prediction, and prediction using the pre-trained model. In the preprocessing phase, we first apply multi-level clustering on the dataset to obtain correlated users and services. We then preprocess the clusters using collaborative filtering to remove the sparsity of the given QoS invocation log matrix. Finally, we create a two-staged, semi-offline regression model using neural networks to predict the QoS value of service to be invoked by a user in real-time. Our experimental results on four publicly available WS-DREAM datasets show the efficiency in terms of accuracy, scalability, fast responsiveness of our framework as compared to the state-of-the-art 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。
