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This study introduces a truthful pricing mechanism for cloud computing resources, optimizing revenue even with unknown job details. The deterministic pricing strategy ensures efficiency and robustness against distribution uncertainties.

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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.