TY - GEN
T1 - Online revenue maximization for server pricing
AU - Boodaghians, Shant
AU - Fusco, Federico
AU - Leonardi, Stefano
AU - Mansour, Yishay
AU - Mehta, Ruta
N1 - Shant Boodaghians and Ruta Mehta were partially supported by NSF grant CCF-1750436. Federico Fusco and Stefano Leonardi were supported by ERC Advanced Grant 788893 AMDROMA \u201CAlgorithmic and Mechanism Design Research in Online Markets\u201D and MIUR PRIN project ALGADIMAR \u201CAlgorithms, Games, and Digital Markets\u201D. Yishay Mansour was supported in part by a grant from the Israel Science Foundation (ISF).
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85097352729
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 4106
EP - 4112
BT - Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
A2 - Bessiere, Christian
PB - International Joint Conferences on Artificial Intelligence
T2 - 29th International Joint Conference on Artificial Intelligence, IJCAI 2020
Y2 - 1 January 2021
ER -