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.
Ce cours fait partie de la Spécialisation Cloud Computing
Concepts de Cloud Computing : Partie 2
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Distributed ComputingC++Cloud ComputingNoSQL
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Approx. 24 heures pour terminer
Recommandé : 5 weeks of study, 5 - 10 hours/week
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Sous-titres : Anglais, Coréen
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Université de l'Illinois à Urbana-Champaign
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.
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Ce cours fait partie du diplôme intégralement en ligne Master in Computer Science de Université de l'Illinois à Urbana-Champaign.
Si vous êtes admis au programme complet, vos cours seront pris en compte dans votre apprentissage diplômant.
Programme du cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
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.
4 heures pour terminer
11 vidéos (Total 122 min), 7 lectures, 2 quiz
11 vidéos
Orientation Towards Cloud Computing Concepts: Some Basic Computer Science Fundamentals23 min
Week 1 Introduction1 min
1.1. The Election Problem8 min
1.2. Ring Leader Election14 min
1.3. Election in Chubby and ZooKeeper9 min
1.4. Bully Algorithm9 min
2.1. Introduction and Basics12 min
2.2. Distributed Mutual Exclusion10 min
2.3. Ricart-Agrawala's Algorithm11 min
2.4. Maekawa's Algorithm and Wrap-Up16 min
7 lectures
Orientation Overview10 min
Syllabus10 min
About the Discussion Forums10 min
Course Learning Community and Social Media10 min
Week 1 Overview10 min
Homework 1 Instructions10 min
Programming Assignment Instructions10 min
2 exercices pour s'entraîner
Orientation Quiz10 min
Homework 120 min
2 heures pour terminer
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.
2 heures pour terminer
8 vidéos (Total 95 min), 2 lectures, 1 quiz
8 vidéos
Week 2 Introduction1 min
1.1. RPCs17 min
1.2. Transactions13 min
1.3. Serial Equivalence12 min
1.4. Pessimistic Concurrency13 min
1.5. Optimistic Concurrency Control9 min
2.1. Replication14 min
2.2. Two-Phase Commit11 min
2 lectures
Week 2 Overview10 min
Homework 2 Instructions10 min
1 exercice pour s'entraîner
Homework 230 min
3 heures pour terminer
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.
3 heures pour terminer
9 vidéos (Total 110 min), 2 lectures, 1 quiz
9 vidéos
Week 3 Introduction2 min
Stream Processing in Storm15 min
Distributed Graph Processing16 min
Structure of Networks21 min
4.1. Single-processor Scheduling11 min
4.2. Hadoop Scheduling11 min
4.3. Dominant-Resource Fair Scheduling8 min
Storm Demo 9 min
Apache Spark by Faria Kalim12 min
2 lectures
Week 3 Overview10 min
Homework 3 Instructions10 min
1 exercice pour s'entraîner
Homework 330 min
3 heures pour terminer
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.
3 heures pour terminer
6 vidéos (Total 109 min), 2 lectures, 1 quiz
6 vidéos
Week 4 Introduction1 min
1.1. File System Abstraction23 min
1.2. NFS and AFS14 min
Distributed Shared Memory17 min
Sensor and Their Networks30 min
Interview with Brighten Godfrey23 min
2 lectures
Week 4 Overview10 min
Homework 4 Instructions10 min
1 exercice pour s'entraîner
Homework 420 min
Avis
4.6
Meilleurs avis pour CONCEPTS DE CLOUD COMPUTING : PARTIE 2
par MRJul 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.
par TCJul 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.
par AKJul 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.
par HGApr 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.
par SJFeb 16, 2019
Would really help to get the correct answers for the questions one failed once the test is passed. The programming assignment could use additional tips and help
par OEAug 15, 2019
Good and informative course. Somewhat more manageable that CCC1; however; the programming assignment was once again a challenge... which is a good thing.
par GGJun 1, 2019
The course gives an advanced overview of the distributed system. The programming assignment was hard to crack but there was a lot of learning involved.
par SRJun 26, 2017
In continuation with Pt. I, this course delves into many more interesting topics. There's a significant scope of self-discovery and learning.
par SCMay 15, 2018
Good course. Gives you a good overview of cloud computing paradigms. Cloud Computing I and II both are quite informative courses.
par MMMar 31, 2018
Awesome class for software engineers. If you haven't take it in college, or need a refresher, this is a great way to do so.
par ALJan 30, 2020
Programming assignment is very immature. I've encountered few segfaults (in provided code, not mine). But the rest is good
par SBOct 16, 2016
Excellent Course! I found both the Cloud Computing Concepts highly informative and relevant for the course objective.
À propos du Spécialisation Cloud Computing
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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