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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.
Cloud Computing implies scalable services and the consolidation of compute and storage resources in large-scale resource centers. This consolidation offers the opportunity to redistribute resources, which have been used so far to keep the own resources running. At the same time, the users of a Cloud infrastructure services have the freedom to use their desired operating systems, programming languages and applications.
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.
The course (lectures, exercises and presentations) is held in English language.
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
The exercise session always takes place on Monday between 15:45-16:15 in room 1-130.
|11.04.2022||Introductory events "Freshmen introductions" of our faculty|
|18.04.2022||Easter Monday. Public holiday|
|25.04.2022||14:15-15:45||1-130||Lecture||Discussion of slide set 1 (slides 1-54)|
|02.05.2022||14:15-15:45||1-130||Lecture||Discussion of slide set 1 (slides 55-67) + slide set 2 (slides 1-15)|
|06.05.2022||15:00-16:00||Zoom||Lecture||Discussion about semester projects|
|09.05.2022||This lecture and the exercise session cannot take place due to a business trip|
|16.05.2022||14:15-15:45||1-130||Lecture||Discussion of slide set 2 (slides 16-49)|
|23.05.2022||14:15-15:45||1-130||Lecture||Discussion of slide set 2 (slides 50-72)|
|06.06.2022||Whit Monday. Public holiday|
|13.06.2022||14:15-15:45||1-130||Lecture||Guest Lecture from Thinkport|
|27.06.2022||14:15-15:45||1-130||Presentation||Semester project presentations|
|04.07.2022||14:15-15:45||1-130||Presentation||Semester project presentations|
|11.07.2022||14:15-15:45||1-130||Presentation||Semester project presentations|
|Slide sets||Exercise sheets||Solutions||Topics|
|Slide set 1||Exercise sheet 1||Solution||Organisational information, Client-Server, Fundamentals, Laws and Limitations, Parallel Computers|
|Slide set 2||Exercise sheet 2||Solution||Cluster Computing|
|Slide set 3||Cloud Computing, Services and Concepts, Opportunities and Risks|
|Slide set 4||Amazon Web Services (EC2, EBS, ELB, S3), Google Cloud Storage, Private Cloud IaaS (Eucalyptus, OpenStack)|
|Slide set 5||MapReduce/Hadoop|
Since WS2021, the cloud computing course does not have a written exam anymore! Your grade will depend 100% on your work and the results of the semester project.
Form a team of four team members maximum and pick a topic. You and your teammates need to...
|1||Nelli Aghajanyan, Fargina Mahmud, Ruchit Dineshbhai Dobariya, Bhargav Anghan||Edge-Computing (Framework: EdgeX)||TBD||TBD|
|2||Moeez Ur Rehman, Muhammad Haseeb Anwar, Sahrish Kanwal, Harmain Haider||Training machine learning models (e.g. for tensorflow) in the Cloud with AWS and Google Cloud services||TBD||TBD|
|3||Parth Desai, Hardikkumar Dudhat, Milan Dabhi, Arpankumar Sabhadiya||TBD (??? Kafka ???)||TBD||TBD|
|4||Navya Sree Kanakala, Melisa Xhepa||Machine Learning model computation in AWS and IBM Cloud services||TBD||TBD|
|5||Raoul Neumann, Tobias Mass, Tobias Schiffhauer||CI/CD of Cloud Functions including the Service by using Infrastructure as Code||TBD||TBD|
|6||Ta-Seen Junaid, Ashis Banik, Shrabanti Saha Rimi, Saiful islam, Mohammad Sayedur Rahman||Containerisation of web application and deploying in public cloud||TBD||TBD|
|7||Nikhil Bajaj, Vedant Asawale||Triggered Training of Machine Learning models in Cloud||TBD||TBD|
|Training machine learning models (e.g. for tensorflow) in the Cloud||Google Cloud Training mit TensorFlow 2, Simple way to deploy machine learning models to cloud|
|Kubernetes (local deployment and oursourcing)||Kubernetes, Google Kubernetes Engine, AWS Kubernetes|
|Private/Hybrid Cloud IaaS Eucalyptus||Project, Code, Blog|
|Private/Hybrid Cloud IaaS OpenStack||Project, Code|
|Private/Hybrid Cloud IaaS OpenNebula||Project, Blog, Code|
|Private/Hybrid Cloud IaaS Apache CloudStack||Project, Code|
|Infrastructure as Code (e.g. with Terraform)||Project, Code|
|Multi Cloud PaaS Cloud Foundry||Project, Code|
|Deployment and comparison of different S3-compatible Private Cloud storage solutions (e.g. MinIO, Swift and Riak CS)||Some ideas...|
|Edge-Computing framework (e.g. EdgeX)||EdgeX, Code|
|CDH 6.3.2 (Cloudera's Distribution Including Apache Hadoop)||Company, Documentation|
|Apache OpenWhisk||Project, Code|
|Your idea !||Cloud Native Interactive Landscape, ???|
The best way to reach me is via email: firstname.lastname@example.org
Prof. Dr. Christian Baun
Frankfurt University of Applied Sciences
(1971-2014: Fachhochschule Frankfurt am Main)
Faculty of Computer Science and Engineering
Last updated: May 16th 2022