Learning Cloud Dynamics to Optimize Spot Instance Bidding Strategies

As infrastructure-as-a-service clouds become more popular, cloud providers face the complicated problem of maximizing their resource utilization by handling the dynamics of user demand. Auction-based pricing, such as Amazon EC2 spot pricing, provides an option for users to use idle resources at highly reduced yet dynamic prices; under such a pricing scheme, users place bids for cloud resources, and the provider chooses a threshold “spot” price above which bids are admitted. In this paper, we propose a nonlinear dynamical system model for the time-evolution of the spot price as a function of latent states that characterize user demand in the spot and on-demand markets. This model enables us to adaptively predict future spot prices given past spot price observations, allowing us to derive user bidding strategies for heterogeneous cloud resources that minimize the cost to complete a job with negligible probability of interruption. Along the way, the model also yields novel, empirically verifiable insights into cloud provider behavior. We experimentally validate our model and bidding strategy on two months of Amazon EC2 spot price data and find that our proposed bidding strategy is up to 4 times closer to the optimal strategy in hindsight compared to a baseline regression approach while incurring the same negligible probability of interruption.