基于学习邻域模型的云服务个性化QoS预测
Personalized QoS Prediction of Cloud Services via Learning Neighborhood-based Model
摘要：
Internet上云服务的爆炸式增长给服务发现和选择带来了新的挑战。特别地，对有效的服务质量（QoS）评估的需求变得迫切强烈。为了解决这个问题，本文提出了基于邻域的方法，利用协同智能的云服务的QoS预测。不同于启发式协同过滤和矩阵分解，我们定义了一个正式的基于邻域的预测框架，它允许一个有效的全局优化方案，然后利用不同的基线估计组件，以提高预测性能。为了验证所提出的方法，一个大规模的QoS特定的数据集，其中包括来自339个服务用户的调用记录在5,825个Web服务的世界级分布式网络。实验结果表明，学习的基于邻域的模型可以克服现有的启发式协同过滤方法的困难，并取得优于国家的最先进的预测方法的性能。
英文摘要：
The explosion of cloud services on the Internet brings new challenges in service discovery and selection. Particularly, the demand for efficient quality-of-service (QoS) evaluation is becoming urgently strong. To address this issue, this paper proposes neighborhood-based approach for QoS prediction of cloud services by taking advantages of collaborative intelligence. Different from heuristic collaborative filtering and matrix factorization, we define a formal neighborhood-based prediction framework which allows an efficient global optimization scheme, and then exploit different baseline estimate component to improve predictive performance. To validate the proposed methods, a large-scale QoS-specific dataset which consists of invocation records from 339 service users on 5,825 web services on a world-scale distributed network is used. Experimental results demonstrate that the learned neighborhood-based models can overcome existing difficulties of heuristic collaborative filtering methods and achieve superior performance than state-of-the-art prediction 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。
