Nane Kratzke

Lecture:

Cloud-native Programming

Published: 01 Jul 2022 (latest update: 01 Jul 2022)
Short: CloudProg
Study: Computer Science/Distributed Systems, M. Sc.
Semester: WS 2022/23

Remark: Google Cloud is asked whether to support this course via Google Teaching Credits. Google already supported the WS 2020/21 and WS 2021/22 courses. Thank you very much. Information on how to make use of these grants will be provided in Moodle. However, this welcome support will not affect the independence of the course content and the critical examination of cloud computing topics such as vendor lock-in or portability issues of cloud-native applications.

The course Cloud-native Programming is given for Master Computer Science students at the Lübeck University of Applied Sciences. It focuses mainly on the programming specifics that are necessary to develop so-called cloud-native applications. Cloud-native applications are intentionally designed to run on cloud infrastructures and leverage the elasticity and scalability features of modern private or public cloud-platforms and -infrastructures by their design.

The course lays the necessary “Everything-as-Code” programming capabilities to understand and create the design modern of cloud-native applications. Corresponding cloud-native application architecture design patterns are covered by the follow-up course “cloud-native architectures”.

The course covers the following aspects and addresses each aspect from a practical development/programming point of view:

  • Fundamentals of Cloud Computing
    • Cloud Computing definitions and point of views
    • Cloud-service models
    • Implications of the “Cloudonomics”
  • Motivation and principles of DevOps
    • DevOps-compliant architectures
    • Continuous disciplines and deployment pipelines
    • Inside Gitlab CI/CD (Type Representative)
  • Infrastructure as a Service
    • Virtualization (Emulation, Type-1/2 Hypervisors, OS-Virtualization, SW-Virtualization)
    • Automatic provisioning approaches
    • The cloud service model Infrastructure as a Service (IaaS)
    • Infrastructure as Code
    • Inside Vagrant and Terraform (Provisioning Type Representatives)
    • Inside Google Compute Engine (IaaS Type Representative)
  • Standardization of deployment units
    • Platform as a Service and the PaaS-Problem
    • Linux-based OS-Virtualization (Containers)
    • Container runtime environments and container patterns
    • Inside Docker (Type Representative)
  • Container Orchestration
    • The scheduling problem and possible approaches
    • The orchestration problem and possible approaches
    • Inside Kubernetes (Type Representative)
    • Inside Google Kubernetes Engine (Managed K8S Service Representative)
  • Function as a Service
    • Serverless
    • Function as a Service Approaches and Platforms
    • Serverless Programming Models
    • Inside Kubeless or OpenWhisk (Self-hosted FaaS Representatives)
    • Inside Google Cloud Functions (Managed FaaS Representative)

Closing remark: This course will likely been given in a hybrid online/presence format composed of online/presence lectures and online/presence practical labs.

Material

Labs

  • Lab 01: Understand Cloud Workloads
  • Lab 02: Deployment Pipelines as Code
  • Lab 03: Immutable Infrastructure
  • Lab 04: Infrastructure as a Service und Infrastructure as Code
  • Lab 05: Containerization
  • Lab 06: Container Orchestration
  • Lab 07: Function as a Service