Optimal prediction of cloud spot instance price utilizing deep learning

Abstract Cloud platforms frequently provide a variety of virtual machine models (VMs) with varying types and capacities, allowing users to select instances that best suit their needs. Cloud providers have devised a system for maximizing the utilization of redundant computing resources, whose costs fluctuate dynamically depending on supply and demand. "Spot pricing" is a common term for this, the user must make a suitable offer that is higher than the spot price to use this instance. Accurate spot price prediction enables users to prepare ahead of time for the bid price and run time to increase the method's reliability. To solve this critical challenge of predicting future pricing, we utilize a consistent and robust deep learning model. When compared to other sophisticated methods, the test results reveal that our proposed method performs better and more accurately.