Current Challenges and Approaches for Resource Demand Estimation in the Cloud

The increasing popularity of Cloud computing, especially for high performance computing (HPC) applications offers a huge potential for optimizing the consumption of compute resources. Since hybrid Cloud platforms in particular offer the best balance between data security, performance, business agility and mobile support, they are used more and more frequently. In this work, we highlight the most important challenges that arise for resource demand estimation systems, especially in public and hybrid Cloud environments. We present existing approaches, separated in load-balancing - or single resource type systems - and Cloud or virtual machine (VM) type selection - or multiple resource type systems. The approaches are analyzed in different aspects including their potential to overcome the presented challenges and their applicability in different Cloud environments. Our research reveals that not all of the issues have been resolved yet but the means to achieve that are available. We conclude our work with useful suggestions that can help to overcome the remaining challenges.