Daleel：使用机器学习简化云实例选择
Daleel: Simplifying Cloud Instance Selection Using Machine Learning
摘要：
由于服务产品和定价模式的多样性，云环境中的决策非常具有挑战性，特别是考虑到云市场是一个令人难以置信的快速发展市场。此外，没有硬性规定，每个客户都有一组特定的约束条件（例如预算）和应用程序要求（例如最低计算资源）。机器学习可以通过执行特定于客户的分析来确定最合适的实例类型以及启动或迁移实例的最佳时间，从而帮助解决一些复杂的决策。我们采用机器学习技术来开发自适应部署策略，在客户需求和可用的云服务产品之间提供最佳匹配。我们提供了一个实验研究的基础上广泛的一套作业执行的主要公共云基础设施。
英文摘要：
Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules, each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
