论文标题
使用绩效发现的长期IAA选择
Long-term IaaS Selection using Performance Discovery
论文作者
论文摘要
我们提出了一个新颖的框架，以根据消费者的长期绩效要求选择IAAS提供商。拟议的框架利用免费的短期试验来发现IaaS提供商的未知QoS绩效。我们设计了一种基于时间天际线的过滤方法，以选择短期试验的候选IaaS提供商。开发了一种新型的合作长期QoS预测方法，该方法利用使用工作量重播技术利用类似消费者的过去试验经验。我们提出了一种新的试验工作量生成模型，该模型在没有过去的试验经验的情况下估计提供商的长期绩效。预测的信心是根据消费者的试验经验来衡量的。基于现实世界数据集进行了一组实验，以评估所提出的框架。
We propose a novel framework to select IaaS providers according to a consumer's long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider's long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework.
