Online revenue maximization for server pricing

Shant Boodaghians,Ruta Mehta,Federico Fusco,Stefano Leonardi,Yishay Mansour

doi:10.1007/s10458-022-09544-y

Abstract

Efficient and truthful mechanisms to price resources on servers/machines have 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 the underlying 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. We also prove that the proposed pricing strategy is robust to imprecise knowledge of the job distribution and that a distribution learned from polynomially many samples is sufficient to obtain a near-optimal truthful pricing strategy.

Highlights

Designing mechanisms for a desired outcome with strategic and selfish agents is an extensively studied problem in economics, with classic work by Myerson [30], and Vickrey–Clarke–Groves [37], for truthful mechanisms
We model the problem of finding a revenue maximizing pricing strategy as a Markov Decision Process (MDP)
We prove that the optimal pricing strategy is monotone in length under a distributional assumption, which we show is satisfied when the jobs’ valuation follows a log-concave distribution, parametrized by length

Summary

Introduction

Designing mechanisms for a desired outcome with strategic and selfish agents is an extensively studied problem in economics, with classic work by Myerson [30], and Vickrey–Clarke–Groves [37], for truthful mechanisms. Beginning with Nisan and Ronen [31], the theoretical computer science community has contributed greatly to the field, in both fundamental problems and specific applications These include designing truthful mechanisms for the maximization of welfare and revenue, and has focused on learning distributions of agent types, menu complexity, and dynamic mechanisms (e.g., [10, 13]). We consider this question in the setting of selling computational resources on remote servers or machines (cf [2, 36]). This leads us to the following question: Can we design an efficient, truthful, and revenue-maximizing mechanism to sell timeslots non-preemptively on a single server?

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