一种风险承担代理模型，用于优化按需和合同虚拟机上的用户请求放置
A Risk-taking Broker Model to Optimise User Requests placement on On-demand and Contract VMs
摘要：
云提供商为最终用户提供各种定价方案，以允许他们根据自己的需求定制VM，例如，即用即付计费方案（称为\textit{on-demand}）和折扣合同方案（称为\textit{reserved instances}）。本文提出了一种云代理，它为用户提供了按需实例的灵活性和在保留实例中找到的一定程度的折扣。代理采用低买高卖策略，将用户请求放入预先购买的折扣云资源的资源池中。通过分析用户请求的时间序列数据，代理采取一种面向风险的方法来动态调整资源池。这种方法不需要在处理大数据流时有用的训练过程。该经纪人使用来自阿里巴巴的高频真实云数据集进行评估。结果表明，经纪人的整体利润是接近理论上的最佳情况下，用户的请求可以完美地预测。
英文摘要：
Cloud providers offer end-users various pricing schemes to allow them to tailor VMs to their needs, e.g., a pay-as-you-go billing scheme, called \textit{on-demand}, and a discounted contract scheme, called \textit{reserved instances}. This paper presents a cloud broker which offers users both the flexibility of on-demand instances and some level of discounts found in reserved instances. The broker employs a buy-low-and-sell-high strategy that places user requests into a resource pool of pre-purchased discounted cloud resources. By analysing user request time-series data, the broker takes a risk-oriented approach to dynamically adjust the resource pool. This approach does not require a training process which is useful at processing the large data stream. The broker is evaluated with high-frequency real cloud datasets from Alibaba. The results show that the overall profit of the broker is close to the theoretical optimal scenario where user requests can be perfectly predicted.
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。
