Infrastructure as a Service (IaaS) is a cloud computing service provided over the internet to facilitate the provisioning of various services such as storage, processes, etc. The provider in the IaaS market may offer some purchasing plans including: reservation, on-demand, and spot plans for its resources. As in real scenarios, demand volume for each plan is assumed to be a random variable with a given probability distribution. The provider maximizes its average revenue in the long run by optimal allocation of its resources among the plans. We formulate an Integer Linear Programming (ILP) model with a stochastic constraint, to determine the number of resources to be allocated for each plan in every time slot in the planning horizon. First, fixed prices are considered for each plan, then two mechanisms of Continuous Double Auction and Second Price Sealed Bid Auction are considered for reservations and spot plans, respectively, to obtain market-driven prices of the services. The Seasonal Weighted Moving Average method is used to predict the amount of demand in every slot. Finally, the proposed mechanisms are evaluated through simulations and the results confirm the effectiveness of the methods in maximizing the revenue and overall utilization of the available IaaS capacity.

基础架构即服务（IaaS）是一种通过互联网提供的云计算服务，以促进各种服务（例如存储，流程等）的供应。IaaS市场中的提供商可能会提供一些购买计划，包括：预订，按需，并确定其资源计划。与实际情况一样，每个计划的需求量均假定为具有给定概率分布的随机变量。从长远来看，提供商可以通过在计划之间最佳分配资源来最大程度地提高其平均收入。我们制定了具有随机约束的整数线性规划（ILP）模型，以确定计划范围内每个时隙中为每个计划分配的资源数量。首先，针对每个计划考虑固定价格，然后考虑两种机制分别考虑进行预订和现货计划的连续两次拍卖和第二次价格密封投标拍卖，以获取服务的市场驱动价格。该加权移动平均季节性方法用于预测在每个时隙需求的量。最后，通过仿真对提出的机制进行了评估，结果证实了该方法在使可用IaaS容量的收益和整体利用率最大化方面的有效性。