Optimal prediction of cloud spot instance price utilizing deep learning

Cloud platforms often offer a variety of virtual machine (VM) models of various types and capacities, enabling users to choose the instances that best meet their requirements. Cloud providers have devised systems to make the most of their redundant computing resources. The cost fluctuates dynamically based on supply and demand. Spot price is a common term for this. To be able to use this instance, the user must create a suitable offer above the spot price. Accurate spot price prediction allows users to pre-prepare bid prices and run time to increase the reliability of the method. For this purpose, we consider Amazon EC2 as a testbed and use its spot price history to predict the future price by constructing a proposed modified gated recurrent unit (MGRU) model and providing a proposed dropout method. Compared with other sophisticated methods, test results show that the proposed method works superior and more accurately.

Automated summary

This study proposes a modified gated recurrent unit (MGRU) model and a dropout method for predicting future prices using the Spot price history of Amazon EC2. The test results show that the proposed method performs superior and more accurately compared to other sophisticated methods.