  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.
