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.
@article{arxiv.2103.07133,
title = {A Risk-taking Broker Model to Optimise User Requests placement on On-demand and Contract VMs},
author = {Chalee Boonprasop and Yuhui Lin and Adam Barker},
journal= {arXiv preprint arXiv:2103.07133},
year = {2021}
}