Cloud computing as a fairly new commercial paradigm, widely investigated by
different researchers, already has a great range of challenges. Pricing is a
major problem in Cloud computing marketplace; as providers are competing to
attract more customers without knowing the pricing policies of each other. To
overcome this lack of knowledge, we model their competition by an
incomplete-information game. Considering the issue, this work proposes a
pricing policy related to the regret minimization algorithm and applies it to
the considered incomplete-information game. Based on the competition based
marketplace of the Cloud, providers update the distribution of their strategies
using the experienced regret. The idea of iteratively applying the algorithm
for updating probabilities of strategies causes the regret get minimized
faster. The experimental results show much more increase in profits of the
providers in comparison with other pricing policies. Besides, the efficiency of
a variety of regret minimization techniques in a simulated marketplace of Cloud
are discussed which have not been observed in the studied literature. Moreover,
return on investment of providers in considered organizations is studied and
promising results appeared.

基于云计算市场的竞争，本研究提出了基于后悔最小化算法的定价策略，并应用于考虑的不完全信息博弈，实验证明该策略相较于其他策略能够大幅增加提供商的利润。此外，文中还讨论了多种后悔最小化技术在仿真云市场中的有效性，并研究了参与组织的投资回报率，取得了有希望的结果。

基于遗憾最小化技术的云市场竞争定价策略

A Competition-based Pricing Strategy in Cloud Markets using Regret  Minimization Techniques

Language models have graduated from being research prototypes to
commercialized products offered as web APIs, and recent works have highlighted
the multilingual capabilities of these products. The API vendors charge their
users based on usage, more specifically on the number of ``tokens'' processed
or generated by the underlying language models. What constitutes a token,
however, is training data and model dependent with a large variance in the
number of tokens required to convey the same information in different
languages. In this work, we analyze the effect of this non-uniformity on the
fairness of an API's pricing policy across languages. We conduct a systematic
analysis of the cost and utility of OpenAI's language model API on multilingual
benchmarks in 22 typologically diverse languages. We show evidence that
speakers of a large number of the supported languages are overcharged while
obtaining poorer results. These speakers tend to also come from regions where
the APIs are less affordable to begin with. Through these analyses, we aim to
increase transparency around language model APIs' pricing policies and
encourage the vendors to make them more equitable.

这项研究分析了语言模型 API 在 22 种语言中的价格策略的公平性，发现一些支持的语言的使用者在使用中被过度收费且结果更差，试图增加对语言模型 API 价格政策的透明度并鼓励供应商使其更加公平。