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.

翻译：云计算作为一种较新的商业范式，已受到不同研究者的广泛关注，但仍面临诸多挑战。定价是云计算市场中的一个主要问题：提供商在相互不知晓对方定价策略的情况下，竞相吸引更多客户。为克服这一信息缺失，我们通过不完全信息博弈对其竞争进行建模。基于此问题，本文提出一种与遗憾最小化算法相关的定价策略，并将其应用于所考虑的不完全信息博弈中。在云计算的竞争型市场中，提供商利用历史遗憾更新其策略分布。反复应用该算法更新策略概率的理念，使得遗憾能够更快地被最小化。实验结果表明，与其他定价策略相比，提供商的利润有大幅提升。此外，本文还讨论了模拟云市场中多种遗憾最小化技术的效率，这在已有文献中尚未被涉及。同时，研究所涉组织中提供商的投资回报率也得到分析，并呈现出令人鼓舞的结果。