Cloud computing systems today, whether open-source or used inside companies, are built using a common set of core techniques, algorithms, and design philosophies—all centered around distributed systems. In this Cloud Computing Concepts, Part 1 course, you will learn about such fundamental distributed computing “concepts” for cloud computing. Some of these concepts include clouds, MapReduce, key-value/NoSQL stores, classical distributed algorithms, widely-used distributed algorithms, scalability, trending areas, and much, much more!
Understand how these techniques work inside today’s most widely-used cloud computing systems. Get your hands dirty using these concepts with provided homework exercises. In the optional programming track, implement some of these concepts in template assignments provided in the C++ programming language.
You will also watch interviews with leading managers and researchers, from both industry and academia.
WEEK 1 – Orientation, Introduction to Clouds, MapReduce
This course is oriented towards learners with similar backgrounds as juniors and seniors in a CS undergraduate curriculum. Since learners come from various backgrounds, it is critical you view this lecture AND pass the prerequisite test. This will ensure you have many of the assumed prerequisite pieces of knowledge required to enjoy this course.
WEEK 2 – Gossip, Membership, and Grids
This module teaches how the multicast problem is solved by using epidemic/gossip protocols. It also teaches analysis of such protocols. Lesson 2: This module covers the design of failure detectors, a key component in any distributed system. Membership protocols, which use failure detectors as components, are also covered. Lesson 3: This module covers Grid computing, an important precursor to cloud computing.
WEEK 3 – P2P Systems
P2P systems: This module teaches the detailed design of two classes of peer-to-peer systems: (a) popular ones including Napster, Gnutella, FastTrack, and BitTorrent; and (b) efficient ones including distributed hash tables (Chord, Pastry, and Kelips). Besides focusing on design, the module also analyzes these systems in detail.
WEEK 4 – Key-Value Stores, Time, and Ordering
This module motivates and teaches the design of key-value/NoSQL storage/database systems. We cover the design of two major industry systems: Apache Cassandra and HBase. We also cover the famous CAP theorem. Lesson 2: Distributed systems are asynchronous, which makes clocks at different machines hard to synchronize. This module first covers various clock synchronization algorithms, and then covers ways of tagging events with causal timestamps that avoid synchronizing clocks. These classical algorithms were invented decades ago, yet are used widely in today’s cloud systems.
WEEK 5 – Classical Distributed Algorithms
This module covers how to calculate a distributed snapshot, leveraging causality again to circumvent the synchronization problem. Lesson 2: This lecture teaches how to order multicasts in any distributed system. Algorithms for assigning timestamp tags to multicasts using various flavors of ordering – FIFO, Causal, and Total – are covered.
The module also covers virtual synchrony, a paradigm that combines reliable multicasts with membership views. Lesson 3: Consensus is one of the most important problems in a distributed system, enabling multiple machines to agree. This module uses Paxos, one of the most popular consensus solutions used in the industry today. Paxos is not perfect because consensus cannot be solved completely – an optional lecture presents the famous FLP proof of the impossibility of consensus.
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