A Systematic Review of Cloud Modeling Languages

Abstract

Modern cloud computing environments support a relatively high degree of automation in service provisioning, which allows cloud service customers (CSCs) to dynamically acquire services required for deploying cloud applications. Cloud modeling languages (CMLs) have been proposed to address the diversity of features provided by cloud computing environments and support different application scenarios, such as migrating existing applications to the cloud, developing new cloud applications, or optimizing them. There is, however, still much debate in the research community on what a CML is, and what aspects of a cloud application and its target cloud computing environment should be modeled by a CML. Furthermore, the distinction between CMLs on a fine-grain level exposing their modeling concepts is rarely made. In this article, we investigate the diverse features currently provided by existing CMLs. We classify and compare them according to a common framework with the goal to support CSCs in selecting the CML that fits the needs of their application scenario and setting. As a result, not only features of existing CMLs are pointed out for which extensive support is already provided but also in which existing CMLs are deficient, thereby suggesting a research agenda.