Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies. CCS Concepts: • Computer systems organization → Cloud computing; • Theory of computation → Semantics and reasoning; • General and reference → Validation.

The specification of deployment topologies for complex applications distributed across multiple heterogeneous infrastructures is a difficult process that encompasses multiple modeling tasks, engaging several actors, including application ops experts, resource experts on the specification of the target infrastructure resources, quality experts on the application optimization, and application administrators on the deployment governance. SODALITE proposes a novel infrastructure as a code (IaC) modeling framework that provides a model driven engineering approach for the authoring of application- and infrastructure-level specifications, realizing an instantiation of an infrastructure as a code (IaC) modeling framework. This chapter introduces the SODALITE IDE and the IaC services. The IDE enables SODALITE expert roles to model (conforming to the SODALITE DSMLs) and generate IaC artefacts facilitating the app deployment. Experts are assisted in the modeling phase by the semantic knowledge inference and validation capabilities of a Knowledge Base (KB), which is populated with IaC descriptions for resources semi-automatically discovered from target heterogeneous infrastructures. The IDE leverages the SODALITE IaC services for automatic target image preparation and IaC artifacts generation upon deployment.

Heterogeneous applications are getting more and more complex, making the authoring of their deployment models an error-prone and demanding task. Heterogeneous resources also make performance optimization of applications complex. In this chapter, we will present the quality assurance and application optimization support of the SODALITE framework, which offers the capabilities for verifying deployment models, detecting bugs and smells in them, and optimizing application components for specific hardware resources. This chapter discusses how the above-mentioned capabilities of the SODALITE framework can be used to develop optimized, defect-free deployment models.