English

Randomized Mechanisms for Selling Reserved Instances in Cloud

Computer Science and Game Theory 2016-11-23 v1 Artificial Intelligence

Abstract

Selling reserved instances (or virtual machines) is a basic service in cloud computing. In this paper, we consider a more flexible pricing model for instance reservation, in which a customer can propose the time length and number of resources of her request, while in today's industry, customers can only choose from several predefined reservation packages. Under this model, we design randomized mechanisms for customers coming online to optimize social welfare and providers' revenue. We first consider a simple case, where the requests from the customers do not vary too much in terms of both length and value density. We design a randomized mechanism that achieves a competitive ratio 142\frac{1}{42} for both \emph{social welfare} and \emph{revenue}, which is a improvement as there is usually no revenue guarantee in previous works such as \cite{azar2015ec,wang2015selling}. This ratio can be improved up to 111\frac{1}{11} when we impose a realistic constraint on the maximum number of resources used by each request. On the hardness side, we show an upper bound 13\frac{1}{3} on competitive ratio for any randomized mechanism. We then extend our mechanism to the general case and achieve a competitive ratio 142logklogT\frac{1}{42\log k\log T} for both social welfare and revenue, where TT is the ratio of the maximum request length to the minimum request length and kk is the ratio of the maximum request value density to the minimum request value density. This result outperforms the previous upper bound 1CkT\frac{1}{CkT} for deterministic mechanisms \cite{wang2015selling}. We also prove an upper bound 2log8kT\frac{2}{\log 8kT} for any randomized mechanism. All the mechanisms we provide are in a greedy style. They are truthful and easy to be integrated into practical cloud systems.

Keywords

Cite

@article{arxiv.1611.07379,
  title  = {Randomized Mechanisms for Selling Reserved Instances in Cloud},
  author = {Jia Zhang and Weidong Ma and Tao Qin and Xiaoming Sun and Tie-Yan Liu},
  journal= {arXiv preprint arXiv:1611.07379},
  year   = {2016}
}

Comments

Already accepted by AAAI'17

R2 v1 2026-06-22T17:01:00.030Z