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ISLA IPGT 26511

Cloud Computing

Computer Engineering
  • ApresentaçãoPresentation
    This course unit gives master's students a technical and critical understanding of cloud computing, from its core concepts to the design, deployment and operation of cloud-based systems. It starts with the service models (IaaS, PaaS, SaaS and FaaS) and deployment models (public, private, hybrid and multi-cloud). It then covers the infrastructure and services of the major providers (AWS, Azure and Google Cloud), virtualisation and containers (Docker and Kubernetes). It also deals with cloud-native application development, using microservices, serverless and edge computing, and with security and compliance: identity and access management (IAM), data protection, the GDPR, ISO/IEC 27001 and NIST. It closes with automation, orchestration and monitoring (Terraform, Ansible, Prometheus, Grafana and CI/CD) and with emerging trends: AI and Big Data in the cloud, quantum computing and energy sustainability.
  • ProgramaProgramme
    CC1: Introduction to Cloud Computing: Concepts, characteristics, and evolution. Deployment models (Public, Private, Hybrid, Multi-Cloud) and service models (IaaS, PaaS, SaaS, FaaS). CC2: Cloud Infrastructure and Services: Major providers (AWS, Azure, Google Cloud). Resource management, distributed storage, virtualization, and containers (Docker, Kubernetes). CC3: Cloud-Native Application Development: Microservices architectures, Serverless and Edge Computing. Cloud application management and monitoring. CC4: Security and Compliance: IAM, authentication, and access control. Data protection, security policies, and standards (GDPR, ISO 27001, NIST). CC5: Orchestration, Automation, and Monitoring: Infrastructure as code (Terraform, Ansible). Monitoring (Prometheus, Grafana, CloudWatch). CI/CD for cloud applications. CC6: Trends and Innovation: AI and Big Data in the Cloud. Quantum Computing. Sustainability and energy efficiency.  
  • ObjectivosObjectives
    At the end of this course unit, students should be able to: LO1: Understand the fundamental concepts of Cloud Computing and its service models (IaaS, PaaS, SaaS).  LO2: Implement and manage Cloud Computing infrastructures using providers such as AWS, Azure, or Google Cloud. LO3: Apply scalable and distributed architectures for cloud system development and deployment. LO4:Evaluate and implement security measures in cloud environments, ensuring data protection and regulatory compliance. LO5: Use automation, monitoring, and orchestration tools in cloud environments, including Kubernetes and Terraform. LO6: Develop cloud-native applications using containers, microservices, and serverless computing.
  • BibliografiaBibliography
    Yanamala, A. K. Y. (2024). Emerging challenges in cloud computing security: A comprehensive review. International Journal of Advanced Engineering Technologies and Innovations, 1(4), 448-479. Sadeeq, M. M., Abdulkareem, N. M., Zeebaree, S. R., Ahmed, D. M., Sami, A. S., & Zebari, R. R. (2021). IoT and Cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 1(2), 1-7. Islam, R., Patamsetti, V., Gadhi, A., Gondu, R. M., Bandaru, C. M., Kesani, S. C., & Abiona, O. (2023). The future of cloud computing: benefits and challenges. International Journal of Communications, Network and System Sciences, 16(4), 53-65. Golightly, L., Chang, V., Xu, Q. A., Gao, X., & Liu, B. S. (2022). Adoption of cloud computing as innovation in the organization. International Journal of Engineering Business Management, 14, 18479790221093992. Rani, S., Bhambri, P., Kataria, A., Khang, A., & Sivaraman, A. K. (2023). Big Data, Cloud Computing and IoT: Tools and Applications  
  • MetodologiaMethodology
    The course follows a theoretical-practical approach, aligned with an active learning model, ensuring solid knowledge and essential technical skills. Theoretical-Practical Classes: Introduction to concepts, deployment models, and cloud services, with discussions and problem-solving. Practical Labs: Simulations with AWS, Azure, and Google Cloud, exploring virtualization, containers, security, and automation. Project-Based Learning: Development of cloud-native applications, applying microservices, Serverless, and infrastructure as code. Case Studies: Analysis of security and compliance scenarios, covering GDPR and ISO 27001. Research and Self-Learning: Exploration of trends such as AI in the Cloud, Quantum Computing, and energy efficiency. This methodology ensures the integration of theory and practice, preparing students for real-world challenges. Classes will be taught in Portuguese and/or English.  
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    6
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não
  • AvaliaçãoEvaluation

     

     

    Descrição Data Limite Ponderação
    Trabalho de Investigação  - Análise de uma tecnologia ou tendência emergente em Cloud Computing.

    04-11-2026

    30%
    Trabalho Prático - Desenvolvimento e implementação de uma aplicação cloud-native.  

    18-11-2026

    40%
    Teste Individual

    09-12-2026

    30%
    Todos os estudantes que não tenham concluído a unidade curricular com sucesso com a avaliação contínua podem realizar um exame final na época de avaliação definida pela instituição. A definir 100%