# A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments
## 2 Review Methodology
### Relevant Surveys
* Extensions of TOSCA that introduce new topologies, management plans, and visual notations. * Methodologies for manipulating TOSCA models. * Papers that compare TOSCA with other specifications or standards. * Papers that present the usage of TOSCA in DevOps (Development and Operations), IoT (Internet of Things), and for testing purposes using TOSCA models. * Papers that introduce TOSCA and its concepts. The above review was conducted during the timeframe from 2013 to 2017, which coincided with a surge in publications related to TOSCA. This period witnessed a peak in the popularity of TOSCA, with numerous works exploring diverse facets of TOSCA and its applications, including:  
* Reports on the utilization of TOSCA;
* Presentations of methodologies for manipulating TOSCA models;
* Extensions of TOSCA. Alongside CMLs, deployment technologies are crucial in defining the components, relationships, and types in cloud deployment models. [PERSON] et al. [7] conduct a systematic review that categorizes deployment technologies into three types:  
* General-purpose deployment technologies, such as Puppet, Chef, Ansible, OpenStack Heat, Terraform, SaltStack, Juju, and Cloudify, can support single-, hybrid-, and multi-cloud deployments, as well as different types of cloud services (XaaS). These technologies are also flexible enough to be customized by integrating personalized and reusable components to cater to additional providers or services. The component's lifecycle can be customized through specific actions, according to [PERSON] et al. 's classification. * Provider-specific deployment technologies, such as AWS (Amazon Web Services) CloudFormation and Azure Resource Manager, are designed for XaaS deployments and support the creation of reusable entities and component lifecycle management. However, unlike General-purpose deployment, these technologies are limited to single-cloud deployments as they are exclusive to specific cloud providers and can only work with the corresponding provider's cloud services, as noted by [PERSON] et al. * Deployment technologies classified as platform-specific support the creation of reusable entities, allow for the management of a component's lifecycle, and can support multiple cloud providers. However, they have certain limitations compared to General-purpose deployment, such as constraints on the cloud delivery model and the requirement of specific platform bundles to implement components. The technologies included in this group, according to [PERSON] et al. 's systematic review, are Kubernetes, CFEngine (Configuration Engine), and Docker Compose. [PERSON] et al. [8] conduct a survey aimed at evaluating the suitability of various CMLs for multi-cloud environments. Their evaluation involves identifying languages that are already multi-cloud enabled and assessing the areas that require extension to support this feature. The assessment criteria include the languages' expressiveness, runtime support, capability to support multi-cloud environments, and model reusability. Our analysis of application concepts in CMLs is influenced by the findings of this survey. In particular, both our research and [PERSON] et al. 's evaluation investigate deployment expressiveness, which pertains to describing the deployment of an application in terms of its structure and content. Additionally, both studies examine the ability to express event-driven descriptions through metric constraints and to bind adaptation actions with workflows. In [PERSON] et al. 's evaluation, CAMEL [1] emerges as the top-performing CML, achieving the highest level of satisfaction across all evaluation dimensions. Notably, this survey encompasses several other CMLs, such as CloudFormation, Azure Resource Manager DSL (Domain-Specific Language), Google Cloud Deployment Manager DSL, Hashicorp Configuration Language, HOT (Heat Orchestration Template), JuJu, and TOSCA. Our analysis aims to combine insights from the aforementioned surveys.