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.
Ce cours fait partie de la Spécialisation Cloud Computing
Concepts de Cloud Computing : Partie 2
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À propos de ce cours
4.6
205 notes
Compétences que vous acquerrez
Distributed ComputingC++Cloud ComputingNoSQL
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
14 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.
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
Semaine
22 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.
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 exercices pour s'entraîner
Homework 230 min
Semaine
33 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.
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 exercices pour s'entraîner
Homework 330 min
Semaine
43 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.
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 exercices pour s'entraîner
Homework 420 min
4.6
42 avisMeilleurs avis
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
Enseignants
Indranil Gupta
ProfessorDepartment of Computer Science
Commencez à travailler pour obtenir votre master
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 sont pris en compte dans votre apprentissage diplômant.
À propos de 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.
À propos de la 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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