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. 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!
Know how these systems work from the inside out. Get your hands dirty using these concepts with provided homework exercises. In the programming assignments, implement some of these concepts in template code (programs) provided in the C++ programming language. Prior experience with C++ is required.
The course also features interviews with leading researchers and managers, from both industry and academia.
This course builds on the material covered in the Cloud Computing Concepts, Part 1 course.
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 4 小时
Week 1: Course Orientation and Classical Distributed Algorithms Continued
Lesson 1: To coordinate machines in a distributed system, this module first looks at classical algorithms for electing a leader, including the Ring algorithm and Bully algorithm. We also cover how Google’s Chubby and Apache Zookeeper solve leader election. Lesson 2: This module covers solutions to the problem of mutual exclusion, which is important for correctness in distributed systems with shared resources. We cover classical algorithms, including Ricart-Agrawala’s algorithm and Maekawa’s algorithm. We also cover Google’s Chubby support for mutual exclusion.
11 个视频 （总计 122 分钟）, 7 个阅读材料, 2 个测验
11 个视频
Orientation Towards Cloud Computing Concepts: Some Basic Computer Science Fundamentals23分钟
Week 1 Introduction1分钟
1.1. The Election Problem8分钟
1.2. Ring Leader Election14分钟
1.3. Election in Chubby and ZooKeeper9分钟
1.4. Bully Algorithm9分钟
2.1. Introduction and Basics12分钟
2.2. Distributed Mutual Exclusion10分钟
2.3. Ricart-Agrawala's Algorithm11分钟
2.4. Maekawa's Algorithm and Wrap-Up16分钟
7 个阅读材料
Orientation Overview10分钟
Syllabus10分钟
About the Discussion Forums10分钟
Course Learning Community and Social Media10分钟
Week 1 Overview10分钟
Homework 1 Instructions10分钟
Programming Assignment Instructions10分钟
2 个练习
Orientation Quiz10分钟
Homework 120分钟
周
2完成时间为 2 小时
Week 2: Concurrency and Replication Control
Lesson 1: Transactions are an important component of many cloud systems today. This module presents building blocks to ensure transactions work as intended, from Remote Procedure Calls (RPCs), to serial equivalence for transactions, to optimistic and pessimistic approaches to concurrency control, to deadlock avoidance/prevention. Lesson 2: This module covers how replication – maintaining copies of the same data at different locations – is used to provide many nines of availability in distributed systems, as well as different techniques for replication and for ensuring transactions commit correctly in spite of replication.
8 个视频 （总计 95 分钟）, 2 个阅读材料, 1 个测验
8 个视频
1.1. RPCs17分钟
1.2. Transactions13分钟
1.3. Serial Equivalence12分钟
1.4. Pessimistic Concurrency13分钟
1.5. Optimistic Concurrency Control9分钟
2.1. Replication14分钟
2.2. Two-Phase Commit11分钟
2 个阅读材料
Week 2 Overview10分钟
Homework 2 Instructions10分钟
1 个练习
Homework 230分钟
周
3完成时间为 3 小时
Week 3: Emerging Paradigms
Lesson 1: We study the emerging area of stream processing, touching on key design aspects of Apache Storm. Lesson 2: We study how enormous graphs can be processed in clouds. Lesson 3: We study various types of networks/graphs that are both natural and artificial, and their surprising commonalities. Lesson 4: This module presents classical scheduling algorithms that have been used in operating systems since the inception of computers. We then cover two popular scheduling algorithms for Hadoop.
9 个视频 （总计 110 分钟）, 2 个阅读材料, 1 个测验
9 个视频
Stream Processing in Storm15分钟
Distributed Graph Processing16分钟
Structure of Networks21分钟
4.1. Single-processor Scheduling11分钟
4.2. Hadoop Scheduling11分钟
4.3. Dominant-Resource Fair Scheduling8分钟
Storm Demo 9分钟
Apache Spark by Faria Kalim12分钟
2 个阅读材料
Week 3 Overview10分钟
Homework 3 Instructions10分钟
1 个练习
Homework 330分钟
周
4完成时间为 3 小时
Week 4: Classical Systems
Lesson 1: When files and directories are stored/accessed over the network, it is called a distributed file system. This module covers the working of distributed file systems like NFS and AFS. Lesson 2: This module covers Distributed Shared Memory systems, their techniques, and pros/cons. Lesson 3: This module looks at the area of sensor networks, starting from what’s inside a sensor mote and how networks of them work.
6 个视频 （总计 109 分钟）, 2 个阅读材料, 1 个测验
6 个视频
1.1. File System Abstraction23分钟
1.2. NFS and AFS14分钟
Distributed Shared Memory17分钟
Sensor and Their Networks30分钟
Interview with Brighten Godfrey23分钟
2 个阅读材料
Week 4 Overview10分钟
Homework 4 Instructions10分钟
1 个练习
Homework 420分钟
4.6
42 个审阅热门审阅
Great course, I would recommend to everyone who wants to understand the basics of cloud computing. The course material is excellent, the instructor Indy is phenomenal and the exams are marvelous.
Very nice and informative course, and the programming assignment in the end of the course is so helpful to understand and apply the theoretical concepts that was introduced during the course
讲师
Indranil Gupta
ProfessorDepartment of Computer Science
立即开始攻读硕士学位
此 课程 隶属于 伊利诺伊大学香槟分校 提供的 100% 在线 Master in Computer Science。如果您被录取参加全部课程，您的课程将计入您的学位学习进程。
关于 伊利诺伊大学香槟分校
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
关于 云计算 专项课程
The Cloud Computing Specialization takes you on a tour through cloud computing systems. We start in in the middle layer with Cloud Computing Concepts covering core distributed systems concepts used inside clouds, move to the upper layer of Cloud Applications and finally to the lower layer of Cloud Networking. We conclude with a project that allows you to apply the skills you've learned throughout the courses.
The first four courses in this Specialization form the lecture component of courses in our online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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