Online Revenue Maximization for Server Pricing
Abstract
Efficient and truthful mechanisms to price time on remote servers/machines have been the subject of much work in recent years due to the importance of the cloud market. This paper considers online revenue maximization for a unit capacity server, when jobs are non preemptive, in the Bayesian setting: at each time step, one job arrives, with parameters drawn from an underlying distribution. We design an efficiently computable truthful posted price mechanism, which maximizes revenue in expectation and in retrospect, up to additive error. The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic. We also show the pricing mechanism is robust to learning the job distribution from samples, where polynomially many samples suffice to obtain near optimal prices.
Cite
Text
Boodaghians et al. "Online Revenue Maximization for Server Pricing." International Joint Conference on Artificial Intelligence, 2020. doi:10.24963/IJCAI.2020/568Markdown
[Boodaghians et al. "Online Revenue Maximization for Server Pricing." International Joint Conference on Artificial Intelligence, 2020.](https://mlanthology.org/ijcai/2020/boodaghians2020ijcai-online/) doi:10.24963/IJCAI.2020/568BibTeX
@inproceedings{boodaghians2020ijcai-online,
title = {{Online Revenue Maximization for Server Pricing}},
author = {Boodaghians, Shant and Fusco, Federico and Leonardi, Stefano and Mansour, Yishay and Mehta, Ruta},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2020},
pages = {4106-4112},
doi = {10.24963/IJCAI.2020/568},
url = {https://mlanthology.org/ijcai/2020/boodaghians2020ijcai-online/}
}