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.

翻译：云端供应商提供各种价格计划,使终端用户能够根据自己的需要量身定制VMs,例如称为\ textit{ on-demand}的现收现付计费计划,以及称为\ textit{reserve situes}的折扣合同计划。本文展示了一个云层经纪人,它既为用户提供了需求情况的灵活性,也为用户提供了保留情况下的某种程度的折扣。经纪人使用一种低价和销售高价战略,将用户请求置于预先购买的折扣云源资源库中。通过分析用户请求时间序列数据,经纪人采取以风险为导向的办法,动态调整资源库。这一办法不需要一个有助于处理大数据流的培训过程。经纪人用Alibaba的高频实际云数据集进行评估。结果显示,经纪人的总体利润接近于理论上的最佳假设,用户请求可以完全预测。