On Estimating Bids for Amazon EC2 Spot Instances Using Time Series Forecasting

Optimum Bid price estimation is crucial for Amazon Elastic Compute Cloud (EC2) consumers if they want to secure uninterrupted access to Spot instances at reduced costs. We recently reported that Bid price estimation is an implicit function of seasonal components and extreme spikes in the Spot price history. In this paper we apply time series forecasting to further substantiate this claim. In particular, we benchmark a number of standard forecasting techniques including Naive, Seasonal Naive, ARIMA, ETS, STL, and TBATS against Spot markets belonging to different market types based on pricing patterns including the presence of seasonal components, extremes, and trends. We run experiments using different look back and forecast horizons, and evaluate the forecasting techniques using three measures, namely Bid Success Rate (BSR), Bid Price Over/Underestimation (BPO/UE), and Root Mean Squared Error (RMSE). Experimental results confirm that successful estimation of Bid prices in EC2 Spot markets is indeed an implicit function of seasonal components and extreme spikes in the Spot price history. Furthermore, our experiments also indicate that for certain types of markets, it is possible to significantly improve BSR by applying a small correction to the estimated Bid price without causing any major disruptions to the market.