Cloud computing offers a distributed computing environment where applications can be deployed and managed. Many companies are seeing substantial interest to extend their technical infrastructure by adopting cloud infrastructures. Although the choice of such an environment may seem advantageous, users are faced with many challenges, especially with regard to deployment and migration of applications in the Cloud. To address some of these challenges, we propose a new approach based on modeldriven engineering techniques (MDE), called MoDAC-Deploy, for the assistance to the configuration and the deployment of applications in the Cloud. This paper focuses on the design and the implementation of our approach. In fact, we developed a model-driven Framework with generative mechanisms to simplify and to automate cloud services deployment process, to overcome APIs heterogeneity, to minimize the vendor lock-in and to enable application portability among different cloud infrastructures by reusing configurations/deployments ”Model a configuration once and deploy it anywhere”. We conducted also a case study in order to validate our proposed approach. Our empirical results demonstrate the effectiveness of our MDE Framework to seamlessly deploy services in the cloud and to migrate easily between different Cloud Service Providers (CSPs) without any programming efforts. Keywords-Deployment; Cloud Computing; Model Driven Engineering.
[1]
Ladan Tahvildari,et al.
A Reference Model for Developing Cloud Applications
,
2011,
CLOSER.
[2]
Gadhgadhi Ridha,et al.
OPENICRA: Towards A Generic Model for Automatic Deployment and Hosting of Applications in the Cloud
,
2013
.
[3]
Jean Bézivin,et al.
Model Driven Engineering: An Emerging Technical Space
,
2005,
GTTSE.
[4]
Kyoungho An,et al.
A Model-driven Approach for Price/Performance Tradeoffs in Cloud-based MapReduce Application Deployment
,
2013,
MDHPCL@MoDELS.
[5]
Ewa Deelman,et al.
Automating Application Deployment in Infrastructure Clouds
,
2011,
2011 IEEE Third International Conference on Cloud Computing Technology and Science.
[6]
Kyoungho An,et al.
Model-driven performance estimation, deployment, and resource management for cloud-hosted services
,
2013,
DSM '13.
[7]
Ladan Tahvildari,et al.
The (5+1) architectural view model for cloud applications
,
2014,
CASCON.
[8]
Richard Wolski,et al.
The Eucalyptus Open-Source Cloud-Computing System
,
2009,
2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.