Cloud Computing Concepts, Part 1

Go to Course: https://www.coursera.org/learn/cloud-computing

Introduction

### Course Review and Recommendation: Cloud Computing Concepts, Part 1 on Coursera **Course Overview** The course titled **Cloud Computing Concepts, Part 1** offers learners a comprehensive introduction to the foundational principles of cloud computing, primarily centered around distributed systems. Whether you're looking to deepen your understanding of cloud technology or are keen to tap into the latest methodologies employed by major tech companies, this course is curated to equip you with a strong theoretical and practical grasp of the field. **Target Audience** This course is designed for learners who possess a foundational background in computer science, akin to that of undergraduates in their junior or senior years. It emphasizes the necessity of a prerequisite knowledge base, underscoring the importance of comprehending core concepts that will be pivotal throughout the course. **Syllabus Breakdown** Here’s a week-by-week breakdown of what you can expect to learn: - **Week 1: Orientation, Introduction to Clouds, MapReduce** This opening week sets the stage with an orientation and provides insights into critical concepts like MapReduce. It serves as an entry point to grasp the fundamental components of cloud computing. - **Week 2: Gossip, Membership, and Grids** In this week, you will explore epidemic protocols that address the multicast problem and understand the roles of failure detectors and membership protocols. Additionally, the course covers grid computing, an essential precursor to modern cloud systems. - **Week 3: P2P Systems** This module dives into the design and functionality of peer-to-peer systems, analyzing both popular (e.g., Napster, BitTorrent) and efficient (e.g., Chord, Pastry) architectures. You’ll gain insights into the structural and operational details that make these systems tick. - **Week 4: Key-Value Stores, Time, and Ordering** This week focuses on NoSQL storage systems like Apache Cassandra and HBase, alongside the critical CAP theorem. You will learn about the challenges of synchronizing distributed clocks and ways to tag events with timestamps, essential for managing data integrity in distributed environments. - **Week 5: Classical Distributed Algorithms** The final week covers the intricacies of distributed snapshots, multicast ordering, and consensus algorithms, with a special emphasis on Paxos—a cornerstone of distributed computing. You’ll also encounter theoretical challenges like the FLP proof, which enriches your understanding of the complexities involved in achieving consensus in distributed systems. **Course Highlights** 1. **Comprehensive Learning**: The structured content gradually builds from basic to advanced concepts, ensuring thorough comprehension. 2. **Real-World Applications**: The course effectively bridges theory and practice, highlighting real-world applications of distributed systems and cloud computing. 3. **Expert Instruction**: Often featuring industry leaders as instructors, the course draws on experienced voices in the cloud computing space, enriching the learning experience with practical insights. **Who Should Take This Course?** - **Computer Science Students**: Ideal for those in their undergraduate studies looking to solidify their understanding of cloud computing and distributed systems. - **Tech Professionals**: Suitable for software developers, system architects, or IT professionals interested in enhancing their knowledge of cloud infrastructures. - **Tech Enthusiasts**: Anyone with a passion for technology and an eagerness to learn more about how current cloud computing models function. **Recommendation** I highly recommend enrolling in **Cloud Computing Concepts, Part 1** on Coursera, especially if you seek a rich blend of theoretical knowledge and practical insights into cloud computing. The cohesive syllabus and well-defined learning outcomes make it beneficial for both students and professionals. By the end of the course, you will possess a robust understanding of the principles underlying cloud computing, equipping you with the skills necessary to navigate and participate in this rapidly evolving field. **Conclusion** Investing your time in this course will provide you with valuable knowledge that can enhance your career trajectory in technology. Join a vibrant community of learners and unlock the complexities of cloud computing today!

Syllabus

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.

Lesson 1: 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.

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.

Lesson 1: 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.

Lesson 1: 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 impossibility of consensus.