TSOS21: First workshop on trustworthy software and open source, March 23-25, 2021, Virtual Conference

A Smart Development Environment for Infrastructure as Code

4th Special Session on High-Performance Services Computing and Internet Technologies (@HPCS2020)

A Pattern-based Semantic Lifting of Cloud and HPC Applications using OWL 2 Meta-modelling

Journal Information and Software Technology Volume 127, November 2020, 106376

Cloud applications monitoring: An industrial study

ScienceDirect - Information and Software Technology Volume 137, September 2021, 106593

The do’s and don’ts of infrastructure code: A systematic gray literature review

Use case: Vehicle IoT

The Vehicle IoT use case builds on ADTP’s KnowGo Car data management and services platform for connected vehicles.

Use case: Vehice IOT

In this deliverable, we report on the implementation progress in the second year of the project, its culmination in the second prototype of the SODALITE platform, and the evaluation of the release through a combination of technical KPI assessment and validation by the project’s three demonstratin

D6.3 Intermediate Implementation and Evaluation of the SODALITE Platform and Use Cases

The SODALITE Framework is the software system that includes all SODALITE stable components. This deliverable includes the description of the software that makes up the SODALITE stack, while the actual software is available through the GitHub SODALITE repository (https://github.com/SODALITE-EU).

D6.6 SODALITE Framework - Second Version

This deliverable reports on the status of the development, at M24, of the SODALITE Runtime Layer and the integration of its components with the rest of the SODALITE platform. This is the second of three deliverables in this series, to be released annually during the project lifetime.

D5.2 Application deployment and dynamic runtime - Intermediate version

The purpose of this deliverable is to present the status of the IaC Management Layer at the end of the second year of the SODALITE project.

D4.2 IaC Management - Intermediate version

This deliverable is the continuation of deliverable D2.1 and provides the consolidated evolution of requirements, KPIs, evaluation plan and architecture over the second year.

D2.2 Requirements, KPIs, evaluation plan and architecture - Intermediate version

Artificial Intelligence (AI) applications based on Deep Neural Networks (DNN) or Deep Learning (DL) have become popular due to their success in solving problems likeimage analysis and speech recognition.

Optimising AI Training Deployments using Graph Compilers and Containers

Deep Leaning and code embedding based approach to linguistic detecting anti-patterns in Infrastructure as code. This is from SODALITE smell and defect prediction task.

DeepIaC: Deep Learning-based Linguistic Anti-Pattern Detection for Infrastructure-as-Code

Deep Leaning and code embedding based approach to linguistic detecting anti-patterns in Infrastructure as code. This is from SODALITE smell and defect prediction task.

DeepIaC: Deep Learning-based Linguistic Anti-Pattern Detection for Infrastructure-as-Code