Cloud computing can provide users with basic hardware resources, and there are three instance types: reserved instances, on-demand instances and spot instances. The price of spot instance is lower than others on average, but it fluctuates according to market demand and supply. When a user requests a spot instance, he/she needs to give a bid. Only if the bid is not lower than the spot price, user can obtain the right to use this instance. Thus, it is very important and challenging to predict the price of spot instance. To this end, we take the most popular and representative Amazon EC2 as a testbed, and use the price history of its spot instance to predict future price by building a k-Nearest Neighbors (kNN) regression model, which is based on our mathematical description of spot instance price prediction problem. We compare our model with Linear Regression (LR), Support Vector Machine Regression (SVR), Random Forest (RF), Multi-layer Perception Regression (MLPR), gcForest, and the experiments show that our model outperforms the others.

中文翻译：

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使用k NN回归的云现货实例价格预测

云计算可以为用户提供基本的硬件资源，并且共有三种实例类型：预留实例，按需实例和竞价型实例。现货实例的价格平均低于其他实例，但会根据市场需求和供应而波动。当用户请求竞价型实例时，他/她需要出价。只有当报价不低于现货价格时，用户才能获得使用该实例的权利。因此，预测现货实例的价格非常重要且具有挑战性。为此，我们将最受欢迎和最具代表性的Amazon EC2作为测试平台，并使用其现货实例的价格历史记录通过建立k-最近邻居（kNN）回归模型，该模型基于我们对现货实例价格预测问题的数学描述。我们将模型与线性回归（LR），支持向量机回归（SVR），随机森林（RF），多层感知回归（MLPR），gcForest进行了比较，实验表明我们的模型优于其他模型。