The goal of this lecture is to discuss and study advanced theoretical foundations, system designs, algorithms and system analysis of recent developments regarding Service Computing and Cloud Computing in large-scale distributed systems. In particular, the courses will focus on advanced algorithms, system models, and performance monitoring and analysis techniques for state-of-the-art cloud services, software service systems, IoT, and edge computing. Students will learn and research state-of-the-art algorithms and techniques through literature study, individual and team development project with real-world distributed systems.

This course presents and studies hot topics in advanced algorithms, system models, and performance monitoring and analysis techniques for state-of-the-art cloud services, software service systems, IoT, and edge computing. From the system perspective, we will focus on

• Complex and hybrid cloud computing systems, such as IoT cloud systems and federated clouds as continuum computing environments where complex applications will be developed and executed.
• Complex and big data analytics  for emerging domains

Several enabling techniques are also discussed for the above-mentioned systems to support user's requirements, such as

• Advanced algorithms for cloud computing, IoT and edge computing
• Elasticity  control algorithms
• End-to-end service analytics covering dependability and performance

This course is highly designed for PhD and master research level

Agenda:

1. Course Overview
• Lecture time: 1h
2. Service-oriented and Cloud Computing: Recap and Outlook
   • Lecture: 2h
   • Self-study:6h
3.  Advanced algorithms for Complex and Hybrid cloud systems
   • Lecture: 2h
   • Self-study and assignment:12h
   • Presentation and discussion: 2h
4. Elasticity principles and control algorithms
   • Lecture: 2h
   • Self-study and assignment:6h
5. Complex big data analytics system designs
   
   • Lecture: 2h
   • Self-study and assignment:12h
   • Presentation and discussion: 2h
6. End-to-End Service Performance and Dependability Analytics
• Lecture: 2h
• Self-study and assignment:12h
• Presentation and discussion: 2h
7. Final exam
• Self-study and preparation: 9h
• Exam: 1h

ECTS-Breakdown:

• 3 ECTS, corresponds to roughly 75 hours of work
• ca. 60% Lecture part, 40% practical part (assignment) = 45h Lecture, 30h Assignments
• Lecture: 11h
• Lecture study: 18h
• Work on assignment topics: 30h
• Presentation and discussion of assignments: 6h
• Exam preparation and exam: 10h
• Total: 45h (Lecture) + 30h (Assignments) = 75h

The total marks for this course is 100 points

• Assignment 1 (individual): 20 points
• Assignment 2 (team): 20 points
• Assignment 3 (individual): 20 points
• Final exam: 40 points
• Special case: 70-100% for students who decide to work on a publishable scientific paper to address selected topics

Grading mapping

• 90-100 points :  1 (Sehr gut)
• 75-89   points :  2 (Gut)
• 56-74   points :  3 (Befriedigend)
• 40-55   points :  4 (Genügend )                   
• 0-40     points :  5 (Nicht Genügend)

It is designed for PhD and master research students.  It is expected that students are familiar with Internet computing, data analytics, distributed systems, and distributed technologies.