# Online Revenue Maximization for Server Pricing

## TL;DR

This paper develops an online posted-price mechanism for server resource pricing that maximizes revenue in a stochastic setting with unknown distributions, ensuring truthfulness and efficiency.

## Contribution

It introduces a computationally efficient, revenue-optimal posted-price mechanism for online server pricing under uncertainty, with provable near-optimality from limited samples.

## Key findings

- The mechanism achieves revenue optimality in expectation and retrospectively.
- A polynomial number of samples suffices for near-optimal pricing.
- Prices are deterministic and depend only on interval length and server availability.

## 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.