A Review on Forecasting Cloud Spot Instances for Elastic Cloud Applications

Cloud services are worldwide used in different kinds of applications. Thereby on these servers are depends upon the use in application and the traffic on applications. In order to execute these services for the applications, the computational resources are required. These computational resources are termed as the Amazon spot instance. As the load and demand of these spot instances varies the prices of these resources are also varies. Additionally, the resources and their utilization in different applications are not predictable due to dependency upon other applications and the demands. In order to understand the price variation for spot instance prices the prediction algorithms are required. The data mining technique provides us the ability to learn with the historical data trends and identify or recognize the similar trends of the data. This ability makes it essential for various new generation applications. This paper presents a comprehensive review on Data Driven Approaches for optimal prediction of cloud spot instances.