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.
课程信息
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您将获得的技能
Distributed ComputingC++Cloud ComputingNoSQL
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 6 门）
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完成时间大约为24 小时
建议：5 weeks of study, 5 - 10 hours/week
英语（English）
字幕：英语（English）, 韩语
提供方
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.
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。
教学大纲 - 您将从这门课程中学到什么
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.
完成时间为 4 小时
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
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.
完成时间为 2 小时
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
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.
完成时间为 3 小时
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
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.
完成时间为 3 小时
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
审阅
4.6
来自云计算基础：第 2 部分的热门评论
由 MR 提供Jul 15, 2017
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.
由 TC 提供Jul 2, 2017
Good, but not a fan of using C++ and that made the programming assignment tricky, especially since can only chnage a few things and the provided code is not always very well explained.
由 AK 提供Jul 12, 2018
I couldn't quite appreciate the utility of topics like sensor networks or structure of networks. Other than that, the course was fine and the programming assignment was interesting.
由 HG 提供Apr 14, 2017
Just the same as the first course. Medium-hard quizzes and a hard programming assigment that is actually harder to understand than to develop.\n\nOverall a five star course.
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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