Modern Distributed Systems
Source: https://learningforlife.tudelft.nl/modern-distributed-systems/ Parent: https://learningforlife.tudelft.nl/our-courses/ai-data-computer-science/programming-software/
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- Analyze and design core architectures, components, and techniques in distributed systems.
- Contrast distributed systems with other forms of computation (e.g., single machine computation, parallel computing).
- Identify applications of distributed systems in science, engineering, business, and home use, and in particular the use of cloud and serverless applications, big data and graph processing applications, interactive and online gaming, etc.
- Solve practical problems related to modern uses of distributed systems.
- Describe the principles of distributed systems.
Free
- Type Course
- Location Online
- Pacing Starts anytime / Self-paced
- Length
For instructor paced courses this is the length of the course.
For self-paced courses this is the length of the course if you spend the amount of time per week as specified. You're free to go faster or slower as you see fit.
6 Weeks - Effort 3 - 5 Hours per week
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- Jan Rellermeyer
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Overview
Distributed systems are the backbone of modern society but entail challenges in areas such as complexity and energy-use. Discover distributed systems from first principles, understand the architectures and techniques derived from them and explore examples of current practical use.
This course will provide learners with a fundamental understanding (theoretical and practical foundations) of how cloud, edge, and big data processing systems work and how they address common challenges for distributed systems such as performance, resilience, and scalability.
Modern IT infrastructure is built as distributed systems, an exciting concept that started with the first computers and evolved rapidly into its present form. From online video meetings to internet services, from social media platforms to online games, we all use and interact with distributed systems on a daily basis and increasingly depend on them. Designing and operating such large-scale distributed systems, however, is complex and typically involves making reasonable compromises. There are fundamental technical barriers as well as economic arguments why we cannot make these systems behave as if they were running on a single, perfectly reliable machine.
In this course, learners will be introduced to the essential functional and non-functional concerns of distributed systems and the common problems encountered while designing them, such as consistency, availability, elasticity, and scalability. A variety of practical solutions that have been established in the leading tech industry in recent years will be reviewed. These provide re-usable building blocks to create new large-scale applications. These recent developments, especially around cloud computing, large-scale data processing, distributed machine learning, and other fields are often not reflected in textbooks and are absent from many traditional curricula but are at the heart of this course.
The learning progress is assessed through a variety of different activities including quizzes, design exercises, experiments, and open questions, with peer review of other students’ solutions. In the final project, learners will design a distributed system based on the learners’ own experience and interests and describe the functional and non-functional properties of the system.
##### Quote from Learner
"I recommend this course to anyone who would like to understand the particular challenges that the development of many distributed applications faces in order to operate correctly. I found the examples of online gaming a very good example to understand how the different technologies involved solve peculiar problems." - Manuel Mendonça, Engineer from Portugal - Details
##### Course Syllabus
Module 1: Introduction to Distributed Systems
- Parallel vs. Distributed Systems
- Challenges in Distributed Systems
- The CAP Theorem
- Example 1: Online Gaming
- Example 2: Scientific Computing
Module 2: Functional Requirements
- Functional vs. Non–Functional Properties
- Naming
- Replication
- Consistency
- Consensus
Module 3: Non-Functional Requirements
- Importance of Performance
- Measuring NFRs: Metrics
- Scalability and Elasticity
- Amdahl’s Law
- Gustafson’s Law
- Benchmarking
Module 4: Resource Management and Scheduling
- Scheduling in the Small: Processor Scheduling
- Scheduling in the Large: Scheduling for Distributed Systems
- Workloads in DS
- Centralized Schedulers: Kubernetes, etc.
- Decentralized Schedulers: Condor, etc.
- Portfolio Scheduling
Module 5: System Architectures and Programming Models
- Trade-offs between SAs and PMs
- Programming Models
- System Architectures: communication, big data, machine learning
- Layering
Module 6: Distributed Ecosystems
- Introduction to massive processing
- Theory of ecosystems
- Super-distribution principle
- Distributed ecosystems in science and engineering: cloud, edge, big data
- Distributed ecosystems in online gaming
- The future of distributed ecosystems
- Qualifications
##### Chartered Engineering Competences
All our online courses and programs have been matched to the competences determined by KIVI’s Competence Structure, a common frame of reference for everyone, across all disciplines, levels and roles.
These competences apply to this course:
- A1: Extend your theoretical knowledge of new and advancing technologies.
- Admission
This is a Massive Open Online Course (MOOC) that runs on edX.\
##### Prerequisites
- Basic knowledge of software systems.
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Basic programming skills in a mainstream programming language.
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Course format: MOOC
This course is a Massive Open Online Course (MOOC). Our MOOCs are delivered on edX.org and are open to all. They include video lectures, readings, assignments, and community discussions. Content is free, with optional certificates and additional exercises available for a fee.
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