Online Revenue Maximization for Server Pricing

Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server....

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Published in:arXiv.org
Main Authors: Boodaghians, Shant, Fusco, Federico, Leonardi, Stefano, Mansour, Yishay, Mehta, Ruta
Format: Paper
Language:English
Published: Ithaca Cornell University Library, arXiv.org 01.10.2019
Subjects:
Computation
Maximization
Optimization
Polynomials
Preempting
Pricing
Revenue
Servers
ISSN:2331-8422
Online Access:Get full text
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Abstract Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from an underlying unknown distribution. We design a posted-price mechanism which can be efficiently computed, and is revenue-optimal 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, depending only on the length of the allotted time interval and on the earliest time the server is available. If the distribution of agent's type is only learned from observing the jobs that are executed, we prove that a polynomial number of samples is sufficient to obtain a near-optimal truthful pricing strategy.
AbstractList Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from an underlying unknown distribution. We design a posted-price mechanism which can be efficiently computed, and is revenue-optimal 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, depending only on the length of the allotted time interval and on the earliest time the server is available. If the distribution of agent's type is only learned from observing the jobs that are executed, we prove that a polynomial number of samples is sufficient to obtain a near-optimal truthful pricing strategy.
Author Leonardi, Stefano
Mansour, Yishay
Boodaghians, Shant
Fusco, Federico
Mehta, Ruta
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SubjectTerms Computation
Maximization
Optimization
Polynomials
Preempting
Pricing
Revenue
Servers
Title Online Revenue Maximization for Server Pricing
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