Toward personalized and adaptive QoS assessments via context awareness
Toward personalized and adaptive QoS assessments via context awareness
复制标题
DOI：
10.1111/coin.12129
复制
发表时间：
2018-05
影响因子：
2.8
通讯作者：
L. Barakat;Phillip Taylor;N. Griffiths;A. Taweel;Michael Luck;S. Miles
中科院分区：
文献类型：
--
作者：
L. Barakat;Phillip Taylor;N. Griffiths;A. Taweel;Michael Luck;S. Miles
服务质量（QoS）属性在区分功能等效的服务和适应用户的不同期望方面起着重要的作用。然而，某些属性的主观性质以及服务环境的动态和不可靠性质可能导致服务提供商所宣传的质量值缺失或不可信的情况。为了解决这个问题，已经提出了许多QoS估计方法，使用服务上可用的观察历史来预测其性能。虽然这种先前的观察（并对应于用户和服务相关的因素）的背景下可以提供一个重要的信息源的QoS估计过程中，它只被用于有限的程度上由现有的方法。作为回应，我们提出了一个上下文感知的质量学习模型，通过一个支持学习的服务代理来实现，利用域的上下文特征来为当前的情况提供更个性化、更准确和更相关的质量估计。实验结果表明，所提出的方法的有效性，在不同类型的不断变化的服务环境中显示出可喜的成果（在预测精度方面）。
Quality of Service (QoS) properties play an important role in distinguishing between functionally equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, using the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been used to a limited extent by existing approaches. In response, we propose a context‐aware quality learning model, realized via a learning‐enabled service agent, exploiting the contextual characteristics of the domain to provide more personalized, accurate, and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.