A distributed system consists of several independent computers that communicate with each other over a network. Different distributed system architectures exist in the computer science. The most popular architecture is the Client-Server model. But especially in the parallel computing field, exist among others, Cluster Computing and Cloud Computing. In this course, the fundamentals of Cloud Computing and related technologies are discussed. Practical exercises are an important part of this course because they are essential for thee understanding of these technologies.

Parts of the slide sets are based on the book Cloud Computing: Web-Based Dynamic IT Services, which was published in 2011 by Springer. ISBN: 978-3-642-20916-1

This course has no written exam! Instead, your grade will depend 100% on your work and the results of the semester project. In previous semesters, each group had worked on completely different themes. This semester, all groups will work on one overarching theme.

The semester project in SS2023 has the goal to develop an edge computing solution for the automatic detection of pets (cats, dogs, golden hamster). The semester project includes:

• development and setup of sensor nodes with single board computers (Raspberry Pi 4) and camera modules (Raspberry Pi Camera Module 8MP v2).
• deployment of the operating system (e.g. Raspberry Pi OS or Ubuntu) and the object detection software (e.g. YOLO or Tensorflow)
• collection of a sufficient number of images for the training and testing of the object detection model (e.g. Tensorflow, OpenCV, YOLO) with own hardware (GPUs!) or with a cloud service (e.g. Roboflow or V7)
• development of a backend to manage the sensor nodes and the collected data. The backend will be deployed as docker container(s) on a Raspberry Pi Kubernetes Cluster with k3s (or a similar solution). The backend Infrastructure will be a robust and scalable High Avalability cluster! of Raspberry Pi 3 nodes. The cluster includes a distributed file system (e.g. Ceph with the Ceph Object Gateway S3 API) or a storage service (e.g. MinIO)
• development of a frontend to present e.g. log information, event messages, and a map of events with proof like images and timestamps. The frontend will be deployed as docker container on a Raspberry Pi Kubernetes Cluster with k3s (or a similar solution) too and should be made with a modern framework (e.g. Vue.js or React)
• use of protocol(s) like REST or MQTT for interaction between the components (frontend, backend, services, etc.)
• optional: Implementation of a Telegram notification feature with a Telegram Bot.

The requirements will be collected in class, discussed, and distributed among the individual teams on April 12th. We will try to design and assign the tasks so that the failure of individual team members and teams does not lead to the collapse of the overall project. It is very important that all participants of the module attend class on the 12th of April!