|Xiaoxi Zhang||The University of Hong Kong, Hong Kong|
|Chuan Wu||The University of Hong Kong, Hong Kong|
|Zhiyi Huang||University of Hong Kong, USA|
|Zongpeng Li||University of Calgary, Canada|
State-of-the-art cloud platforms adopt pay-as-you-go pricing, where users pay for the resources on demand according to occupation time. Simple and intuitive as it is, such a pricing scheme is a mismatch for new workloads today such as large-scale machine learning, whose completion time is hard to estimate beforehand. To supplement existing cloud pricing schemes, we propose an occupation-oblivious online pricing mechanism for cloud jobs without pre-specified time duration and for users who prefer a predetermined cost for job execution. Our strategy posts unit resource prices upon user arrival and decides a fixed charge for completing the user's job, without the need to know how long the job is to occupy the requested resources. At the core of our design is a novel multi-armed bandit based online learning algorithm for estimating unknown input by exploration and exploitation of past resource sales, and deciding resource prices to maximize profit of the cloud provider in an online setting. Our online learning algorithm achieves a low regret sub-linear with the time horizon, in terms of overall provider profit, compared with an omniscient benchmark. We also conduct trace-driven simulations to verify efficacy of the algorithm in real-world settings.