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.

Online revenue maximization for server pricing / Boodaghians, Shant; Fusco, Federico; Leonardi, Stefano; Mansour, Yishay; Mehta, Ruta. - (2020), pp. 4106-4112. (Intervento presentato al convegno Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence tenutosi a Yokohama, Japan (Virtual Conference)) [10.24963/ijcai.2020/568].

Online revenue maximization for server pricing

Boodaghians, Shant;Fusco, Federico;Leonardi, Stefano;
2020

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.
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Mechanism design; posted price; Markov decision processes
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Online revenue maximization for server pricing / Boodaghians, Shant; Fusco, Federico; Leonardi, Stefano; Mansour, Yishay; Mehta, Ruta. - (2020), pp. 4106-4112. (Intervento presentato al convegno Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence tenutosi a Yokohama, Japan (Virtual Conference)) [10.24963/ijcai.2020/568].
File allegati a questo prodotto
File Dimensione Formato  
Boodaghians_Online-revenue_2020.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 153.52 kB
Formato Adobe PDF
153.52 kB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1460893
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 1
social impact