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首页> 外文期刊>Information Sciences: An International Journal >QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset
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QoS prediction for web service compositions using kernel-based quantile estimation with online adaptation of the constant offset

机译：使用基于内核的分位数估计以及常数偏移的在线自适应，对Web服务组合进行QoS预测

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摘要

Services offered in a commercial context are expected to deliver certain levels of quality, typically contracted in a service level agreement (SLA) between the service provider and consumer. To prevent monetary penalties and loss of reputation by violating SLAs, it is important that the service provider can accurately estimate the Quality of Service (QoS) of all its provided (composite) services. This paper proposes a technique for predicting whether the execution of a service composition will be compliant with service level objectives (SLOs). We make three main contributions. First, we propose a simulation technique based on Petri nets to generate composite time series using monitored QoS data of its elementary services. This techniques preserves time related information and takes mutual dependencies between participating services into account. Second, we propose a kernelbased quantile estimator with online adaptation of the constant offset to predict future QoS values. The kernel-based quantile estimator is a powerful non-linear black-box regressor that (i) solves a convex optimization problem, (ii) is robust, and (iii) is consistent to the Bayes risk under rather weak assumptions. The online adaption guarantees that under certain assumptions the number of times the predicted value is worse than the actual value converges to the quantile value specified in the SLO. Third, we introduce two performance indicators for comparing different QoS prediction algorithms. Our validation in the context of two case studies shows that the proposed algorithms outperform existing approaches by drastically reducing the violation frequency of the SLA while maximizing the usage of the candidate services.
机译：预期在商业环境中提供的服务将提供一定水平的质量，通常是在服务提供商和消费者之间签订的服务水平协议（SLA）中约定的。为了防止违反SLA的行为而导致金钱惩罚和声誉损失，重要的是服务提供商必须准确估计其所有提供的（复合）服务的服务质量（QoS）。本文提出了一种用于预测服务组合的执行是否符合服务级别目标（SLO）的技术。我们做出三个主要贡献。首先，我们提出了一种基于Petri网的仿真技术，以利用其基本服务的QoS数据生成复合时间序列。该技术保留了与时间有关的信息，并考虑了参与服务之间的相互依赖性。其次，我们提出了一种基于内核的分位数估计器，该常数估计器可以在线匹配常数偏移量以预测未来的QoS值。基于核的分位数估计器是一个功能强大的非线性黑盒回归器，它（i）解决凸优化问题，（ii）鲁棒，并且（iii）在相当弱的假设下与贝叶斯风险一致。在线调整可确保在某些假设下，预测值比实际值更差的次数收敛到SLO中指定的分位数。第三，我们介绍了两个性能指标，用于比较不同的QoS预测算法。在两个案例研究的背景下，我们的验证表明，通过最大程度地降低SLA的违规频率，同时最大程度地利用候选服务，所提出的算法优于现有方法。

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