Cloud and Edge Computing

The direction structure has been defined based on the support of specific Cloud worker roles and more specifically

• Cloud/Edge Systems Engineer: this role is responsible for building and/or managing the core computing infrastructure (infrastructure layer, service platforms and networks) in a public and/or private cloud/edge. It is divided into sub-roles responsible for the maintenance and monitoring of systems (Cloud Operations Engineer) and for the implementation of systems and methodologies for automated application and infrastructure management (Cloud DevOps Engineer).
• Cloud/Edge Developer: the role is responsible for creating the core code of applications, but based on design practices and patterns inherent to Cloud Computing, Edge Computing and IoT. The requirements and capabilities of how AI, Edge and IoT operate dictate that applications should be designed and implemented in a more flexible way than typical applications, so that they can take advantage of the elasticity of infrastructure and services. As such, they require radically different design in areas such as state management, scaling and de-scaling, dynamic configuration and adaptation to current execution conditions. Also their increased distributed nature creates more needs for fault tolerance and optimization of their operation. In addition, the transformation of an architecture from monolithic to microservices and/or serverless logic architecture requires code mapping and porting to the new computing models, as well as the cooperation between the application code and the provided Cloud or Edge services.
• Cloud Solutions Architect: is required to provide solutions for the design and use of cloud and edge services, taking into account the specificities and needs of a particular use case, the specificities and capabilities of the key service layers and how they can be combined or satisfy the specific use case, both in terms of the technological aspect of adaptation and evolution of existing applications and in terms of the economic aspect. Therefore, the Cloud Solutions Architect in today's landscape is required to propose solutions and strategies, select the provider and type of service requested (e.g. Private vs. Public Cloud) and act as a holistic digital transition advisor towards Cloud and IoT services and technologies.

  Overall, the specialization emphasizes the study and use of open-source tools, enabling our students to become familiar with their design details, architectures, and internal operation. These tools form the backend of widely used commercial cloud services, allowing students to gain experience both as Cloud users/developers and as Cloud Providers/Cloud Administrators. 

  In addition, emphasis is placed on combining the theoretical and practical (laboratory) components of the curriculum. Students have the opportunity to experiment using the University's available infrastructure, including the data and platform of the University's IoT Smart Campus, featuring more than 200 sensors and 670,000 data values per day.

  Students may also choose courses from the other specializations, depending on the additional expertise they wish to acquire. For example, students wishing to specialize in optimizing the production operation of Cloud systems and applications may select courses from the AI specialization (Machine Learning and Deep Learning), while those interested in the techno-economic aspects of systems design may choose courses from the Information Systems Management specialization (Technology Economics & Investment Appraisal and Business Process Management).