Prediction of Atomic Web Services Reliability for QoS-Aware Recommendation
Prediction of Atomic Web Services Reliability for QoS-Aware Recommendation
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DOI：
10.1109/tsc.2014.2346492
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发表时间：
2015-05-01
影响因子：
8.1
通讯作者：
Srbljic, Sinisa
中科院分区：
文献类型：
--
作者：
Silic, Marin;Delac, Goran;Srbljic, Sinisa
在构建基于面向服务系统的qos感知复合工作流时，有必要评估潜在服务选择候选对象的非功能属性。在本文中，我们介绍了CLUS，一个用于原子web服务可靠性预测的模型，它基于从以前的调用中收集的数据来估计正在进行的服务调用的可靠性。为了提高当前最先进的预测模型的准确性，我们合并了调用上下文的特定于用户服务和环境的参数。为了减少最先进方法中存在的可伸缩性问题，我们使用K-means聚类算法聚合过去的调用数据。为了评估模型的不同质量方面，我们对部署在亚马逊云的不同区域的服务进行了实验。评估结果证实，与目前最先进的方法相比，我们的模型产生了更可扩展和更准确的预测。
While constructing QoS-aware composite work-flows based on service oriented systems, it is necessary to assess nonfunctional properties of potential service selection candidates. In this paper, we present CLUS, a model for reliability prediction of atomic web services that estimates the reliability for an ongoing service invocation based on the data assembled from previous invocations. With the aim to improve the accuracy of the current state-of-the-art prediction models, we incorporate user-service-, and environment-specific parameters of the invocation context. To reduce the scalability issues present in the state-of-the-art approaches, we aggregate the past invocation data using K-means clustering algorithm. In order to evaluate different quality aspects of our model, we conducted experiments on services deployed in different regions of the Amazon cloud. The evaluation results confirm that our model produces more scalable and accurate predictions when compared to the current state-of-the-art approaches.