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Adaptive Cross-Cloud Service Composition with the Bidirectional QoS Updates

机译:双向QoS更新的自适应跨云服务组合

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With the increasing number of cloud services from different providers, service composition across clouds in dynamic environments presents significant challenges. The fact that each of the cloud services has different values of QoS attributes further increases the complexity. Particularly, current approaches cannot efficiently deal with situations when failure between a pair of composed services occurs or the QoS values of the cloud services change in distributed service executions. In this paper, an adaptive cloud service composition in distributed service executions is proposed to improve the traditional Q-learning model and algorithm. It uses a method called the bidirectional QoS updates on composite services which simultaneously considers the QoS update probability and the time cost of selecting the optimal cloud service pairs. The experimental results show that this approach has higher average cumulative rewards and lower average service composition time than the existing approaches.
机译:随着来自不同提供商的云服务数量的增加,动态环境中跨云的服务组合提出了巨大的挑战。每个云服务具有不同的QoS属性值的事实进一步增加了复杂性。特别地,当一对组合服务之间发生故障或云服务的QoS值在分布式服务执行中发生变化时,当前的方法无法有效地处理这种情况。本文提出了一种分布式服务执行中的自适应云服务组合,以改进传统的Q学习模型和算法。它对复合服务使用一种称为双向QoS更新的方法,该方法同时考虑QoS更新概率和选择最佳云服务对的时间成本。实验结果表明,与现有方法相比,该方法具有较高的平均累积奖励和较短的平均服务组合时间。

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