Comprehensive Statistical Analysis and Modeling of Spot Instances in Public Cloud Environments

Due to increase in demand for utilizing public Cloud resources, we are facing with many trade-offs between price, performance and recently reliability. Amazon’s Spot Instances (SIs) provide a low price yet less reliable and competitive bidding option for the public Cloud users. Although some works have explored the utilization of SIs to decrease the monetary cost of Cloud computing, the characteristics of SIs have not been investigated yet. In this paper, we provide a comprehensive statistical analysis and modeling of such SIs based on one year price history in four data centers of Amazon’s EC2. For this purpose, we analyze all different types of SIs in terms of spot price and the inter-price time (time between price changes). Moreover, we determine the time dynamics for spot price in hour-in-day and day-of-week. The results reveal that we are able to model spot price dynamics as well as the inter-price time of each SI by the mixture of Gaussians distribution with three or four components. The proposed models are validated through extensive simulations, which demonstrate that our models exhibit a good degree of accuracy under realistic working conditions. We believe that this characterization is fundamental in the design of stochastic scheduling algorithms and fault tolerant mechanisms in public Cloud environments for spot market.