Online revenue maximization for server pricing

Area of Science:

• Computer Science
• Economics
• Operations Research

Background:

• The cloud computing market necessitates efficient and truthful resource pricing mechanisms.
• Online stochastic settings with non-preemptive jobs and unit capacity servers present pricing challenges.
• Revenue maximization is a key objective in server resource allocation.

Purpose of the Study:

• To design a revenue-optimal and truthful posted-price mechanism for resource allocation in an online stochastic setting.
• To ensure the mechanism is efficiently computable and robust to imprecise knowledge of job distributions.
• To determine the sample complexity required for learning a near-optimal truthful pricing strategy.

Main Methods:

• Development of a deterministic posted-price mechanism.
• Analysis of revenue optimality in expectation and in retrospect.
• Proof of robustness to imprecise job distribution knowledge.
• Demonstration of sufficiency of polynomially many samples for learning.

Main Results:

• A novel posted-price mechanism is proposed that is revenue-optimal up to additive error.
• The pricing scheme is deterministic, depending only on time interval length and server availability.
• The mechanism is robust to imperfect knowledge of the underlying job distribution.
• Near-optimal truthful pricing can be achieved with a distribution learned from polynomial samples.

Conclusions:

• The developed posted-price mechanism offers an efficient and truthful solution for revenue maximization in cloud resource pricing.
• The strategy's robustness to distribution uncertainty and its sample efficiency make it practical for real-world applications.
• This work advances the understanding of truthful mechanism design in online stochastic environments.