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.
2020, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence Main track, Pages 4106-4112
Online revenue maximization for server pricing (04b Atto di convegno in volume)
Boodaghians Shant, Fusco Federico, Leonardi Stefano, Mansour Yishay, Mehta Ruta
Gruppo di ricerca: Algorithms and Data Science