2021/03/12 by Boonprasop, Chalee, Lin, Yuhui, Barker, Adam
#Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #Parallel #Systems and Control (eess.SY) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2103.07133
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 on-demand, and a discounted contract scheme, called 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.