Search-based QoS ranking prediction for web services in cloud environments
Search-based QoS ranking prediction for web services in cloud environments
复制标题
云环境中基于搜索的Web服务QoS排名预测
DOI：
10.1016/j.future.2015.01.008
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发表时间：
2015-09-01
影响因子：
7.5
通讯作者：
Xie, Xiaoyuan
中科院分区：
文献类型：
--
作者：
Mao, Chengying;Chen, Jifu;Xie, Xiaoyuan
与传统的服务质量(QOS)值预测不同，Qos排名预测检查针对特定用户考虑的服务的顺序。为了解决这个NP-完全问题，基于贪婪策略的解决方案被广泛采用，例如CloudRank算法。然而，它们只能产生局部近似解。在本文中，我们提出了一个基于搜索的预测框架来解决服务质量排名问题。传统的粒子群优化(PSO)算法被用来根据服务的服务质量记录来优化服务的顺序。在实际情况中，给定用户的服务质量记录通常是不完整的，因此来自邻近用户的相关数据经常被用来确定服务之间的偏好关系。为了过滤特定用户的邻居，我们提出了一种改进的方法，通过考虑服务对的出现概率来度量两个用户之间的相似性。在相似度计算的基础上，选择前k个邻居为服务排名评估提供服务质量信息支持。定义了有序服务序列的适应度函数来指导搜索算法寻找高质量的排序结果，并提出了初始解选择和陷阱逃逸等策略。为了验证该方法的有效性，在真实的服务质量数据上进行了实验研究，实验结果表明，与现有的CloudRank算法相比，基于粒子群优化算法的方法具有更好的服务排名，而且在大多数情况下，改进在统计上是显著的。(C)2015爱思唯尔B.V.保留所有权利。
Unlike traditional quality of service (QoS) value prediction, QoS ranking prediction examines the order of services under consideration for a particular user. To address this NP-Complete problem, greedy strategy-based solutions, such as CloudRank algorithm, have been widely adopted. However, they can only produce locally approximate solutions. In this paper, we propose a search-based prediction framework to address the QoS ranking problem. The traditional particle swarm optimization (PSO) algorithm has been adapted to optimize the order of services according to their QoS records. In real situations, QoS records for a given consumer are often incomplete, so the related data from close neighbour users is often used to determine preference relations among services. In order to filter the neighbours for a specific user, we present an improved method for measuring the similarity between two users by considering the occurrence probability of service pairs. Based on the similarity computation, the top-k neighbours are selected to provide QoS information support for evaluation of the service ranking. A fitness function for an ordered service sequence is defined to guide search algorithm to find high-quality ranking results, and some additional strategies, such as initial solution selection and trap escaping, are also presented. To validate the effectiveness of our proposed solution, experimental studies have been performed on real-world QoS data, the results from which show that our PSO-based approach has a better ranking for services than that computed by the existing CloudRank algorithm, and that the improvement is statistically significant, in most cases. (C) 2015 Elsevier B.V. All rights reserved.