A Novel Statistical and Neural Network Combined Approach for the Cloud Spot Market

The instance price in the Amazon EC2 spot model is often much lower than in the on-demand counterpart. However, this price reduction comes with a decrease in the availability guarantees. To our knowledge, there is no work that accurately captures the short-term trade-off between spot price and availability, and does long-term analysis for spot price tendencies in favor of user decision making. In this work, we propose a utility-based strategy, that balances cost and availability of spot instances and is targeted to short-term analysis;and a LSTM neural network framework for long term spot price tendency analysis. Our experiments show that, for r4.2xlarge, 90% of spot bid suggestions ensured at least 5.73 hours of availability, with a bid price of approximately 38% of the on-demand price. The LSTM experiments predicted spot price tendencies for several instance types with low error. Our LSTM framework predicted an average value of 0.19 USD/hour for the r5.2xlarge instance type, which is about 37% of the on-demand price. Finally, we used our combined mechanism on an application that compares thousands of SARS-CoV-2 sequences and show that our approach is able to provide good choices of instances, with low bids and very good availability. IEEE