Cloud Native And Dev Ops (CNADO)

Technical Track Scope

Motivation: Applications based cloud native applications (CNAs), are increasing their popularity in software development. CNA-based systems are rooted in the paradigm of utility computing: external providers offer compute power, storage, and network bandwidth for self-service customers. These systems allow new paradigms to develop systems, in particular due to the elasticity and resilience these systems can offer. Elastic systems try to match the used resources as closely as possible to the amount of resources needed to deliver the software-based service for a dedicated work and load situation. When architecting these systems, software architects need to take the new flexibility in the system’s deployment into account and design their system in a way that it uses its resources as optimally as possible.

CNA are applications built to fit seamlessly into the cloud landscape by using all the capabilities provided. Different ingredients can be used in building and construing CNA recipes. Examples might be Microservices, Serverless technologies, including Function as a Service, but also different containerization technologies.

Given the potential benefits of AI, an increasing number of organizations are investigating the best methods to leverage cloud and Edge for AI models training and decision inference.

CNADO aims to bring together cloud, AI and Dev Ops researches from different communities to promote discussions and collaborations. The goal is to help disseminate novel CNA computing practices and solutions as well as identify future challenges and dimensions. To this end, the CNADO track will foster a more interactive participation model, in which authors, invited speakers and attendees will be encouraged to socially engage beyond the planned workshop activities.

CNADO is looking for full research papers, short papers, industrial reports, tool demo papers, and posters.


Topics include, but are not limited, to:

  • Cloud-Based Systems and DevOps
    • Design for elasticity and resilience
    • Evaluation of quality attributes (elasticity, efficiency)
    • Optimizing deployments
    • Multi-Cloud Systems
    • Instrumentation and monitoring of cloud-based systems
    • Fog and Edge Computing
  • Microservices, Serverless and Nano Services
    • Designing systems
    • Patterns and best practices
    • Migration
    • Management and monitoring
    • Maintenance
    • Benchmarking
    • Orchestration
    • Conteinerization
    • Unikernels
  • AI on the cloud
    • Distribution of computation in the cloud
    • Efficient execution of AI on the cloud and Edge
    • AI@Edge
  • Business Aspects of Cloud and DevOps
    • Serviceability
    • Business models

Track Organizers

Davide Taibi,, Tampere University, Finland
Nabil El Ioini,, Free University of Bolzano, Italy
Andrea Alexander Janes,, Free University of Bolzano, Italy

Program Committee

  • Luciano Baresi, Politecnico di Milano, Italy
  • Steffen Becker, University of Stuttgart, Germany
  • Justus Bogner, University of Stuttgart, Germany
  • Antonio Brogi, University of Pisa, Italy
  • Filipe Correia, University of Porto, Portugal
  • Sören Frey, Daimler TSS GmbH, Germany
  • Petr Hnetynka, Charles University in Prague, Czech Republic
  • Andrea Janes, Free University of Bolzano, Italy
  • Ali Khajeh-Hosseini, RightScale, Inc., USA
  • Xiaodong Liu, Edinburgh Napier University, Scotland
  • Jacopo Mauro, University of Southern Denmark, Denmark
  • Claus Pahl, Free University of Bozen-Bolzano, Italy
  • Dana Petcu, West University of Timisoara, Romania
  • Florian Rademacher, University of Applied Sciences and Arts Dortmund, Germany
  • Americo Sampaio, University of Fortaleza, Italy
  • Kari Systä, Tampere University of Technology, Finland
  • Damian Andrew Tamburri, Eindhoven University of Technology – Jeronimus Academy of Data Science, The Netherlands